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Translating multisemiotic texts: The case of audio introductions for the performing arts

Nina Reviers, Hanne Roofthooft, and Aline Remael, University of Antwerp

ABSTRACT

In the field of Media Accessibility, audio introductions (AIs) are usually approached as an addition to audio description (AD) services for the blind and visually impaired. The present paper, however, approaches AIs as a research topic and translation service in its own right. AIs are a unique type of media accessibility service that contributes to the translation of multisemiotic works, and which has not received much attention within academia. As more different and complex performances are being produced and described, practitioners and researchers are constantly facing new challenges. Against this background, the present paper first discusses the possible functions of AIs, based on a qualitative analysis of a multilingual corpus of AIs. Subsequently, the presence and relative weight of these functions is analysed on the basis of the AIs to two Dutch contemporary theatre performances. They demonstrate how varying AI strategies are deployed by describers to fulfil different functions in different contexts, reflecting the demands of the performance at hand.

KEYWORDS

Theatre studies, media accessibility, audio description, integrated accessibility, functional Translation Studies.

1. Introduction

In the field of Media Accessibility (MA), audio introductions (AIs) are generally treated as an integral part of audio description (AD) services for the blind and visually impaired. The present paper, however, approaches AIs as a research topic and translation service in its own right. AIs are a unique type of media accessibility service that contributes to adapting multisemiotic works of art to varying audience needs. In the ADLAB1 AD guidelines (Remael et al. 2014) an audio introduction is defined as:

[A] continuous piece of prose, providing factual and visual information about an audiovisual product, such as a film or theatre performance, that serves as a framework for blind and visually impaired patrons to (better) understand and appreciate a given ST [source text].

Traditional AIs enhance the AD of a live stage performance and provide people who are blind or visually impaired with an audio version of the information available in the programme booklet and/or on the theatre website. In addition, AIs tend to give a brief description of the setting and costumes (see also York 2007; Holland 2009). The aim of AIs is to help audiences understand and appreciate a performance with AD (Cabeza I Caceres 2010) and serve as “pathways towards the enjoyment of an audiovisual text” (Di Giovanni 2014).

Only a handful of publications have been dedicated specifically to AIs and a few articles about AD more generally mention the practice as well. York (2007) and Holland (2009) were among the first to publish on the subject, describing the creation process of AIs based on best practice examples from the UK. Next, a series of articles was published on Catalan experiences with opera AD, including the role of AIs (Orero and Matamala 2007; Cabeza I Caceres 2010, Corral and Llado 2011). Romero Fresco and Fryer (2013) as well as Di Giovanni (2014) report on experiments in the UK and Italy respectively, where AIs were tested with users as an access service for film. Fryer and Romero Fresco’s (2014) book chapter is the first to take a global view, studying AIs as an access service in its own right for both live and recorded media.

Today, the use of AIs is diversifying. They may be designed to serve varying additional functions, catering for different audience types, types of performances and different contexts. The experiments conducted with AIs for film are a case in point and they aim to help audiences grasp the director’s cinematographic style (Fryer and Romero Fresco 2014; Di Giovanni 2014). Describers have also experimented with AIs combining traditional descriptions by a narrator with sound bites of the performance, or interviews with directors or actors, in a style reminiscent of podcasts. Another example is the use of AIs for television series. The Flemish Public Broadcaster VRT has published a series of podcasts on their website with variable success (Remael 2016). These online podcasts provide AD audiences with background information on the series, its locations, settings and characters.

Research, however, has not kept up with these recent developments in practice. As Di Giovanni (2014) has also highlighted, AIs merit more systematic, scientific analysis, in order to critically analyse the varying types of introductions and creation processes that are emerging and that are currently being implemented without systematic reflection on the impact of these new developments on the translation process, product and end users. An interdisciplinary approach taking insights from Theatre Studies into account is a crucial factor in this respect. When studying AIs, key questions arise relating to the nature of the source text and to the (im)possibility of interpretation, which have been food for thought in many subdisciplines within the field of Translation Studies. In order to discuss these issues in the case of AIs for stage performances, however, the specific nature of theatre has to be taken into account. Theatre Studies and Translation Studies scholars alike agree that the difficult and even problematic nature of theatre translation is largely due to the intersemiotic or intermedial character of theatre itself. The combination of word, action, image, music, and sometimes even smell and touch, make theatre an interdisciplinary art form – leading theatre scholar Kattenbelt (2008) to describe theatre most famously as a ‘hypermedium,’ a complex construction built on a layering of different meanings.

While the present paper focusses on AD and AI specifically, it will become clear that this Media Accessibility type constitutes a unique starting point for the further conceptual exploration of Media Accessibility, Audiovisual Translation and Translation Studies more generally.

2. Theoretical framework

The present article departs from the main principles of Functional Translation Studies (FTS). The functional approach is not new to audiovisual translation and it has proved its merit for the study of the multimodal nature of media accessibility and AD (Mazur 2019; Reviers 2018; Vercauteren 2016; Remael et al. 2014; Reviers 2012). One of the key tenets of Functional Translation Studies is the impact of the context of a communicative situation on the translation process. In the case of media accessibility, this concerns mostly the shifts in the intended target audience and the (multimodal) communicative medium used. These shifts shape the Skopos or intended function of the translation, which in turn guides the translation process and influences the translation strategies adopted (Nord 2005; Reviers 2018). Following this approach, our aim is to uncover some of the potential variables that influence the communicative context of AIs, their creation process, potential functions and description strategies. As Fryer (2019) underlined, ADs and AIs for the performing arts are all about tailor-made solutions, adapting the chosen strategy to fit the requirements of the context of a given situation.

However, the Functional Translation Studies notions of text and context as fixed, clear-cut concepts have been a source of debate in many Translation Studies subfields, as their boundaries are constantly being questioned and extended and have therefore become increasingly blurred (House 2006; Tymoczko 2019). This is certainly the case for AD and AI of theatre and opera performances.

Traditionally, the performance text largely coincided with the dramatic text or script on which a performance was based (Lehmann 2011). Today, however, the notion of text in the context of theatre and opera has expanded to indicate the full range of verbal and non-verbal semiotic modes and materials that inspire or that make up a stage performance. When discussing this topic, most theatre scholars make reference to Lehmann (2006) and his use of the term ‘postdramatic theatre’. This movement in theatre studies takes a completely different approach to the dramatic text and the use of language in theatre overall. The most prominent bearer of meaning is no longer considered to be (purely) textual. Lehmann extends the notion of text in the following manner: “The theatre performance turns the behavior onstage and in the auditorium into a joint text, a ‘text’ even if there is no spoken dialogue on stage or between actors and audience.” In this view, the AD and AI of a theatre performance should also be “bound to the reading of this total text” (Lehmann 2006: 17).

Similarly, in media accessibility the notion of (source) texthas become an umbrella-term to signify the full range of semiotic resources that describers use as input for their AD and AI. This can include elements from the immediate physical environment – such as audience reactions or any unexpected event that might occur during a live performance – or additional (audiovisual) resources such as the programme booklet, online information about the production or reviews of the performance in (printed or digital) media. In the case of AIs, these ‘paratexts’ constitute an equally important source for translation as the performance itself. ‘Paratext’ is a book-based concept introduced by Genette in 1982, which has been used in a slightly adapted definition in the context of audiovisual translation as well (see O’Sullivan 2018, for instance) to signify the textual materials surrounding a translation.

In the emerging media accessibility field of Integrated Accessibility (also called Accessible Filmmaking for screen products or even Accessible ‘Theatre making’ for stage performances, see Fryer 2019) the question of what constitutes the (source or target) text is blurred even further. Integrated accessibility advocates the integration of accessibility services such as AD and sub- or surtitling for the deaf and hard of hearing (SDH) or sign language interpreting (SLI) in the creation process of films, series or performances (Romero Fresco 2019). In such cases, the source text is no longer limited to one or several text products, but includes elements of the entire creation process:

It [integrated AD, AI] is conceived from the start of the creative process with the full support and involvement of the artistic team […]. IAD should be considered less as a neutral way of conveying the source text and more as a creative tool, with the aim of ‘connecting both audience and performer to each other and the artistic content of a piece in a positive way’ (Fryer 2018: 173).

In the case of AD and AI for contemporary theatre and opera, one of the main motivations for taking into consideration the entire creation process of a production concerns the challenges related to analysing and interpreting contemporary performances. Theatre is unstable by nature. As the theatre scholar Kattenbelt (2008: 33) writes: “Unlike film and television, theatre always takes place in the absolute presence of here and now. The performer and the spectator are physically present at the same time in the same space.” As a result, no two performances of the same production are alike, nor any two interpretations by different visitors. In Theatre Studies, this observation is often related to the concept of ‘theatricality’. Within Theatre Studies theatricality is a key word, but the concept has taken on and continues to take on many different meanings. The undefinable nature of the concept is a given in Theatre Studies and mirrors the unstable nature of theatre performances themselves. As Davis and Postlewait (2003: 1) write:

One thing, but perhaps only one, is obvious: the idea of theatricality has achieved an extraordinary range of meanings, making it everything from an act to an attitude, a style to a semiotic system, a medium to a message.

In addition, research on theatricality is often written from a dominant visual perspective. Claims such as “theatre is perspective” (Verschaffel 1990: 36) or “theatricality is a process of looking at or being looked at” (Féral and Bermingham 2002: 98) are very common. Interdisciplinary challenges, then, arise when one tries to connect the inherent theatrical qualities of a performance (the source text of AD/AI) with traditional media accessibility approaches, which are ‘occularcentric’ as well. In fact, this visually-focused approach to AD has recently also been criticised in the field of Media Accessibility (Fryer 2018).

The question, then, of how the theatrical qualities of a performance and its meaning potential for audiences can remain intact or continue to function in an audio described version or in the AI is a crucial one. Particularly, as translated into the strategies describers develop to deal with a performance’s instability, its theatricality and visual representation and its new target audience.

In practice, this question has led to the emergence of varying types of AIs and creation processes, as mentioned in the introduction. Traditionally, an AI precedes an AD, and is created by the describers who also make the AD. The AI and AD are created in ‘post-production’, i.e., after the performance is created, based on one or two performances or rehearsals, a script and some background research on the website of the theatre house/group, and the programme booklet. Mostly, describers are freelancers who are hired by the venue or festival, and who work relatively independently from the theatre makers who rarely provide editorial input for the AI and AD. In other words, describers have a relatively high level of freedom in creating their translations. There are very few guidelines or conventions in this field and new creation processes are arising constantly to meet the needs of each specific situation. How these developments are studied in this article, is discussed in the next section.

3. Methodology

Against this theoretical background, the following sections proceed to discuss a series of case studies for theatre and opera AI. The discussion consists of two parts. First, Section 4 reports on an exploratory study partly based on a Master’s dissertation supervised by Nina Reviers and Aline Remael, and presented at the 2013 ARSAD conference in Barcelona. It analyses the functions of AIs, based on an in-depth text analysis of a multilingual corpus of 52 AIs (Van Gool 2012; Reviers and Remael 2013) and highlights the potential functions of this type of translation.

Second, Section 5 explores how these functions emerge in different types of AIs and different contexts, by way of an in-depth discussion of two concrete examples of AIs written by two of the authors of the present article. The first example, is the AI of War and Turpentine(Jan Lauwers/Needcompany, 2017). It was written by Hanne Roofthooft as part of her PhD projecton the experience of theatricality for people with a visual impairment. The second example is the AI for the opera performance Satyagraha(Cherkaoui 2017), which is part of a series of as yet unpublished case studies by Nina Reviers and professional describer Victoria Hopchet of three performances with AD, AI and audio subtitling (AST) at the Flemish Opera and Ballet House in Antwerp between 2016 and 2018. The rationale behind the selection of these two case studies is that the situational context in which they were created differs significantly and, therefore, allows for an exploration of how contextual variables possibly influence the functions and form of AIs. In addition, both AIs are more recent than the ones included in the corpus discussed in Section 4 and are illustrative of the new approaches to AI that are emerging. These contextual differences are discussed in detail in Section 5.

In addition, both AIs were created by two of the authors of the present article as part of ongoing participatory research projects in which they fulfil the role of what is often called a ‘practisearcher’ in the field of Audiovisual Translation, a term first coined by Gile (1995). For this reason, the authors had access to all the source materials for analysis, details about the creation process and a unique perspective on the rationale behind the choices of the describers. In such a research context in particular, the background and perspective of the ‘practisearchers’ has an influence on the AI creation process, on the one hand, and the analysis of the results, on the other hand, and must be taken into account. The discussion in Section 5 is based on textual analysis, and on self-reflective inquiry on the part of the practitioners-cum-researchers. First, in Section 4, we discuss the possible functions of AIs in general.

4. The functions of AIs

Mazur (2019; Forthcoming) identified five possible functions or text types for audiovisual translation from a functional perspective: informative, narrative, expressive, persuasive, and light entertaining. Audiovisual texts and their translations can fulfil one or more of these functions simultaneously: an informative documentary, for instance, will often have one or even more narratives. In previous (unpublished) research by the authors of the present article a similar set of functions was identified specifically for AIs (Reviers and Remael 2013; Reviers and Roofthooft 2018) based on a qualitative analysis of a corpus of 52 AIs from 2009-2012. The corpus contained AIs from different geographical areas – United Kingdom, Germany, Italy, Belgium (NL and FR) and Catalonia – professional as well as student describers, with varying approaches to their creation. The qualitative analysis focused on identifying the types of content included in the AIs, and their structural or textual organisation, identifying several overarching function(s).

The structure and the length of introductions did not only differ from one geographical area to the next, it also differed considerably within the work of one describer. While some recurring patterns could be identified (such as the plot summary usually preceding the description of costumes and set), the order of the content elements in the AIs seemed to be adapted to the specific needs of the performances under study.

In terms of the content of AIs, the following recurring categories were observed: general information about the event (describers, theatre venue, length of the performance, special announcements), general information about the piece (genre, translation, history of the performance, rewrite or adaptation, author/writer/composer/playwright, libretto), information about the specific production at hand (credits), information about the plot, scenography (set design and lighting) and characters.

Based on these types of content, three distinctive functions were discerned. AIs first and foremost have an informative role, that is, the introduction provides listeners with different types of general information about the production that is not based on the performance as such, but instead based on its paratexts (website, programme booklet, desk research). As opposed to Mazur’s (2019) aforementioned classification, narrative aspects of plot development were included under this informative function in the initial study, because the descriptions of the plot were brief and factual. The plot descriptions were not a necessary requirement for understanding the AD in these cases, but part of the general information about the production. In later cases, such as the Opera AD of Satyagraha discussed in Section 5, the relative importance of the description of narrative aspects in relation to the AD transpired to be more significant and points to a more strategic decision by the describers. Following Mazur (2019) we therefore argue at present that narrative information can be considered a separate category as well, depending on its relative importance with respect to a specific AD.

In addition to their informative and narrative function, the AIs in the original corpus all had a foreshadowing function. A large portion of the introductions under study contain descriptions of (mostly visual) information that anticipates the performance, because it could not be included in the actual AD due to time restrictions. This often entailed a description of the set, lighting, the characters, their physical characteristics and costumes.

Many AIs also fulfil an explanatory function when describers decide to explain particular aspects of the play because they are considered too complex to grasp without an explicit clarification. Examples include characters or props that are not visualised on stage but implied or referred to in the diegesis or technical aspects of how specific theatrical illusions are created. Consider the following example from Van de Velde, J’aimerais Mieux de bouche vous le dire [sic] (Olympique Dramatique 2012):

Het verhaal wordt gebracht als een toneelstuk binnen een toneelstuk. Dit wordt duidelijk doordat de acteurs soms met opzet uit hun rol vallen, elkaar op momenten bij de echte naam noemen en de kostuumwissels altijd op het podium zelf plaatsvinden.

The story is presented as a play within a play. This becomes clear because the actors occasionally start speaking as themselves, address each other by their real names and because they always change costumes on stage.

In many cases the explanatory strategies in the corpus had an expressive dimension, mentioning the (subjective) effect of a certain theatrical sign, such as impressions created by the setting or lighting. The following example is taken from Bloed en Rozen [Blood and Roses] (Cassiers 2011):

De 13 personages […] dragen kostuums ontworpen door Tim Van Steenbergen. Het zijn overdadige en donkere kostuums, waarin zwart en leer domineren. Er zijn extra mouwen op de schouders genaaid. Dit maakt de kostuums extreem zwaar en grotesk. Bovendien werden houten handen van etalagepoppen aan de mouwen bevestigd. Het is alsof de personages omringd zijn door geesten die vanuit het donker naar de personages grijpen en aan hen trekken.

The 13 characters […] are wearing costumes designed by Tim Van Steenbergen. The costumes are elaborate, dark affairs in which black and leather dominate. Additional pairs of sleeves have been sewn on to the shoulders. This makes the costumes extremely heavy and grotesque. Moreover, the wooden hands of mannequins have been attached to the sleeves. It is as if the characters are surrounded by ghosts who try to grab and pull at the characters from the dark.

Similar to the narrative function mentioned above, the expressive dimension of the explanatory function could be considered as a separate function in many cases, particularly given the fact that the expressive dimension of AIs has gained ground more recently, as will be illustrated later on in this section and in Section 5.

Finally, all AIs in the original AI corpus had an instructive function, i.e., providing information to listeners about, for instance, how to use the headphones for transmitting the AD, the location of accessible toilets or the ticket hall, or, in the case of pre-recorded AI, getting to the venue by public transport. As a recent study by VocalEyes indicated (Fryer 2020) this function is still considered an important aspect of AIs for many users as this type of information cannot be accessed easily elsewhere (online, for instance).

Even though the above results are based on a relatively small corpus dating from 2012, the identified functions have proven to be a relevant framework for analysis and practice (Remael et al. 2014; Fryer and Romero Fresco 2014). As the practice of AIs continuously evolves, the number and type of functions will naturally change as well, but most notable is that our subsequent case-studies mentioned above (see Section 2)have mainly indicated a change in the relative weight of the functions in different contexts. For instance, attention seems to be shifting from the predominantly informative function of AIs to their role in conveying the narrative, visual and dramaturgic style of a performance. In addition, more different types and more complex performances are being described, confronting practitioners and researchers with new challenges (e.g., multilingual performances, the integration of subtitles, hybrid texts combining theatre, opera, modern dance, integration of video and podcasts in performances, etc.). This often results in AIs that extend their reach beyond the factual, informative style that was clearly dominant in the AI corpus initially studied, in favour of the explanatory function and in particular the expressive dimension. This is a development that mirrors the evolution in theatre and the impact of the concept of theatricality in contemporary theatre in particular, as mentioned in Section 2. These recent trends will be further illustrated by the case studies discussed in Section 5.

5. Comparative discussion of case studies

This section discusses and compares two case studies. The first case study is the Dutch production Oorlog en terpentijn [War and Turpentine] premiered on December 7, 2017 at Toneelhuis in Antwerp and is a co-production of Jan Lauwers/Needcompany and Toneelhuis. From the moment War and Turpentine premiered, a pre-recorded, stand-alone audio introduction was available online. This AI was written by Hanne Roofthooft as part of her PhD project.

The second case study, Satyagraha, is an opera originally created by American composer Philip Glass in the 1980s. The remake under study was choreographed by Sidi Larbi Cherkaoui. The opera premiered in April 2017 in Basel, Switzerland, and was a co-production between Theater Basel and Komische Oper Berlin. The performance discussed in this article was staged with AD and an AI at the Flemish Ballet and Opera in Ghent, Belgium, on November 15 and November 28, 2018. The Dutch AD and AI were written and performed by practisearcher Nina Reviers and freelance describer Victoria Hopchet. The AI was not pre-recorded and only read out live by the describers half an hour before the start of the performance and during the breaks.

The discussion below is structured following the text analytical approach from Functional Translation Studies (see Section 2). For both case studies, we discuss and contrast the elements that are part and parcel of this approach: (1) relevant contextual variables, such as the translation brief creation approach and the AD team, (2) relevant source text challenges, including multisemiotic and technical issues, (3) target text functions and (4) AD strategies.

5.1 Contextual analysis: Creation process

As indicated above, War and Turpentine premiered and was performed at Toneelhuis in Antwerp. Toneelhuis has been offering performances with traditional AD and AI yearly since 2009. Professional describers are hired on the initiative of the front office (rather than the artistic team). In addition to these traditional ADs, Toneelhuis started working with Hanne Roofthooft in 2013 as part of her PhD project, to critically analyse their audio described performances. This collaboration led to the joint decision to start offering stand-alone, pre-recorded audio introductions for some of their performances, targeting all audiences. Hanne Roofthooft has a background in Theatre Studies and she drew on this background as well as her research for the stand-alone AIs that she created for the theatre. Apart from this project, Hanne (henceforth referred to as ‘the describer’) does not work as an audio describer. Thanks to her previous connections with the theatre, she had the opportunity to talk to a member of the creative team for War and Turpentine and to witness two rehearsals close to opening night. It is important to note that for War and Turpentine, a traditional AD with complementary AI specifically targeting the visually impaired was also provided, by the regular team of freelance describers. The two AIs were developed independently from one another.

Satyagrahawas performed with live AD and a live delivered AI at the Flemish Opera and Ballet, specifically aimed at blind and visually impaired visitors. The Flemish Opera has been working with the University of Antwerp since 2015 to provide one or two performances a year with AD and AI. The descriptions are made by scholar and professional describer Nina Reviers and professional freelance describer Victoria Hopchet, who were both hired and paid for their work. Both have been trained as audiovisual translators and describers and have several years of experience in film and theatre AD. Nina Reviers is, in addition, a translation scholar specialising in AD. For Satyagraha, the describers wrote an AD with complementary AI, as opposed to the stand-alone AI for War and Turpentine discussed in this article.

Finally, it is important to note that the describers were in both cases given freedom in the way they made the AI. While in the case of War and Turpentine, the artistic team provided input, it was ultimately the describers of both case studies alone who decided on the content and style of the AI. The creative team did not perform a final edit nor did they formally approve the AI before the performance. As stated before, AD and AI are relatively new services for which theatres usually do not have clear procedures and guidelines yet. The responsibility for the AD and AI quality and content rests solely with the describers.

5.2 Source text analysis

According to the functionalist approach to translation, the variables in the context discussed above can potentially influence the translation process. Indeed, the different approaches to the AIs discussed above, also influenced how the describers dealt with their ‘source text’ in both case studies, which, as we have pointed out earlier, is itself a problematic concept for theatre translation.

5.2.1 Identifying the source text

Interest in the preparations that precede a performance, the dramaturgical materials and the creation process, is an expanding field in Theatre Studies. This interest includes the study of the material trail of the creation process, especially but not exclusively, when a performance is not based on one initial dramatic text, which happens more and more often in post-dramatic and contemporary theatre. This burgeoning field of study is referred to in theatre studies as ‘genesis’ studies: “[T]he expectation that insight in the genesis of the performance will provide a more encompassing perspective on the work as a whole” (Le Roy et al. 2016: 468). In addition, theatre scholars are increasingly focusing on methods for grasping and preserving aspects of the unique live experience that is theatre, since they are capturing something that only exists in the here and now (see the concept of theatricality mentioned in Section 2). Hodgdon (2012: 373) writes that “although performance itself exists only as memory, dream, (mis)recollection, the archives are jam-packed full with its material remains.” These material remains are described by Hodgdon as aspects of ‘re-performance’, since they make you relive the performance. Reason (2003: 86) clarifies this process by saying that “the performance wasn’t really what was happening on stage but what is happening in the minds and subsequently the memories of the audience”. The idea that theatre can only exist in the ‘here and now’ questions its translatability. Is an AI consequently always incorrect, or, in other words, does it only represent a partial translation or a new creation?

For War and Turpentine, the input of the creative team and the rehearsals that the describer observed constituted the source text. Since the AI premiered at Toneelhuis at the same time as the performance, there was no finished product available that could be considered a source text. This was very different in the case of Satyagraha, which was a reprise so the describers did not have access to the original creation process, rehearsals, or the artistic team, but they did have access to some ‘remains’, such as the libretto, the surtitles and video recordings. Initially, the describers of the opera did want to take an integrated approach to the AD and AI and work as closely together with the artistic team as possible. Unfortunately, circumstances imposed an entirely different scenario. Since the opera had already premiered in another country before the describers were contacted, only a limited number of rehearsals had still been planned in Flanders. To make matters worse, several of these rehearsals were rescheduled and/or did not eventuate as planned. In order to prepare their AI (and AD), the describers, therefore needed to turn to the dramaturgical materials and paratexts of the ‘re-performance’ as a basis for their translation. This different nature of the source text resulted in a different approach to the analysis and understanding of the performance.

5.2.2 Understanding the source text

The creation process of the recorded AI for War and Turpentine was conceived in line with the integrated accessibility approach mentioned in Section 2. The describer not only consulted a wide range of materials or paratexts, but attended two regular rehearsals, as well as the dress rehearsal, and consulted the dramaturge of the performance, who also provided feedback on the draft version of the AI. This approach offered inroads into the creator’s intentions and allowed for a deeper understanding of the varying layers of meaning in the performance, which often went well beyond what happened visibly on stage.

For instance, in War and Turpentine the stage is filled with people and objects and the audience receives an overwhelming amount of visual information, which makes selecting the most essential items for an AI extremely difficult. The performance is divided into three parts from a narrative point of view, all three of them very different in style. The first part of the narration is set in the years before the war. During this part of the story, the stage is further divided into three different layers. The woman who tells the story – the narrator –, a painter and a woman dressed as a nurse take place at the front of the stage. The back part of the stage is raised on a platform of about half a meter. All the actors remain within their designated area during the first part of the narrative, making the boundaries between the different areas, each representing a different layer of meaning, very explicit. During the second part of the performance, the lights are bright and the scene is more open or spacious. This part of the narrative takes place during the war. The memories of the battlefield are told by words projected on wooden screens at the back of the stage. The third and last part of the performance takes place at the front of the stage only. This part of the narration, taking place after the war, is told by the narrator and performed by the actors simultaneously. The whole storyline is based on the novel War and Turpentine by Stefan Hertmans. The performance also features some very prominent music by the Dutch composer Rombout Willems that is performed live by three musicians. The three musicians, together with the five performers behind the curtain, represent a group of people during every part of the performance, even though their identity is not always made explicitly clear from a narrative point of view. In brief, the stage design contributes strongly to how the narrative(s) is/are to be interpreted. Moreover, how the story is staged is as important as the story it tells. It is a major part of the theatrical experience.

Satyagraha poses a different set of challenges. It is a contemporary opera, based on religious and philosophical writings, and which integrates elements of modern dance, as explained in the following excerpt from the AI:

Satyagraha is de leer van Mahatma Gandhi en betekent zo veel als “De kracht van de waarheid”. Deze leer stelt dat geweldloze burgerlijke opstand het beste wapen is tegen onrecht. Glass vertaalt de ideeën van Gandhi naar het toneel, door er een reeks associatieve beelden van te maken, die elkaar niet-chronologisch opvolgen. De voorstelling voelt, zo zegt Glass zelf, als het doorbladeren van een foto-album. 
Deze beeldenreeks wordt ondersteund door een libretto in het Sanskriet. Het libretto is gebaseerd op een van de belangrijkste teksten uit het Hindoeïsme, namelijk de Baghavad Gitta of het “Lied van de heer”. Dit gedicht weerspiegelt een gesprek tussen een prins Arjuna en de God van de oorlog Krishna. Een dialoog die doet nadenken over de morele dilemma’s die samengaan met oorlog, strijd of opstand. Naast een repetitieve en minimale muziekstijl, worden deze filosofische ideeën op het podium belichaamd door de vloeiende bewegingstaal van tientallen dansers, onder leiding van regisseur en choreograaf Sidi Larbi Cherkaoui. Het resultaat is een suggestieve en meditatieve voorstelling die het publiek aanzet te bezinnen over de filosofie van Satyagraha.

[Satyagraha refers to the teachings of Mahatma Gandhi and means as much as ‘The power of truth.’ This teaching states that non-violent civil rebellion is the best weapon against injustice. Glass translates Gandhi's ideas for the stage by creating a series of associative images that follow one another in non-chronological succession. This series of images is supported by a libretto in Sanskrit. The libretto is based on one of the most important texts of Hinduism, namely the Baghavad Gitta or the ‘Song of the Lord’. This poem reflects a conversation between a Prince Aylen and the God of War Krishna, a dialogue that makes one reflect on the moral dilemmas associated with war, struggle or rebellion. These philosophical ideas are suggested by music that is repetitive and minimalistic in style and they are embodied on stage by the flowing language of movements performed by dozens of dancers, led by director and choreographer Sidi Larbi Cherkaoui. The result is a suggestive and meditative performance that encourages the audience to reflect on Satyagraha's philosophy.]

The excerpt above highlights the main challenges for the description of Satyagraha. Describing modern dance is still a new and experimental field and requires describers to move beyond the factual and objective descriptions that have been dominating AD practices for years (Fertier 2017: 88; Fryer 2009). In addition, the complex, hybrid nature of Satyagraha challenges describers as to its interpretation and their understanding of the maker’s artistic expression. The fact that the present describers did not have access either to the creation process or to the rehearsals, and could not consult the artistic team, meant that their interpretation and understanding of the text was solely dependent on their analysis of the dramaturgical materials.

A final challenge for Satyagraha, was the limited space for the description which had to walk a tightrope between describing the dance sequences and the actions on stage, and voicing the libretto, since the opera was performed in Sanskrit with Dutch subtitles that were inaccessible for the target audience of the AD. This is a key difference with the AI for War and Turpentine, which, as a stand-alone AI, did not have to take into account any time constraints during the AD when selecting what to include. In the next section, we will illustrate how the above (con)textual differences between both texts transpired in the functions the respective AIs expressed.

5.3 The functions of the AIs

The pre-recorded AI for War and Turpentine targeted all possible visitors and was designed to be complementary to the information or paratexts already available to the general public. Therefore, it deliberately did not reproduce or summarise that information, contrary to what is often the case in traditional AIs. Instead, the main aim of this AI was to offer all audiences more explicit inroads into the concept, visual style and structure of the performance – a type of information not explicitly available through other channels yet. As a result, the AI was designed to have an explanatory, expressive function, and the relative importance of the informative and narrative function was minimal. It was assumed audiences could find that information elsewhere, either online or, in the case of the visually impaired, in the ‘traditional AI’ provided with the AD of the performance, provided by the usual team of freelance describers2.

The choice to prioritise the explanatory and expressive functions was not only inspired by the target audience but by the nature of the performance, as discussed in Section 5.2.2. The way in which the theatrical narrative was told was considered to be just as important as the narrative itself in this particular performance, a feature that was also highlighted by the dramaturg. The describer and the dramaturg together looked for tools to help audiences not necessarily understand the story and the setting, but rather fully experience the performance. Describing the visual style of the performance and explaining key visual moments were thought to add an extra layer to the theatrical experience, giving audience members the opportunity to interpret the performance in their own way.

Similarly, the unique (con)textual features of Satyagraha forced the describers to consciously deliberate about the functions of the AI, but this then led to very different choices. A first key difference with the previous case study is that the foreshadowing function of the AI was of great importance, given that the actual description during the performance would have to be largely improvised due to the lack of rehearsal time. The fact that the AI had to be combined with an AD greatly restricted the freedom to choose what information to include, as compared to War and Turpentine. This will be further illustrated in Section 5.4.

Second, Satyagraha posed specific challenges in terms of interpretation, due to the impressionistic nature of the performance and the integration of modern dance. Feedback and input from the creative team and insight into the creation process, which would have greatly helped in understanding the performance, had been impossible to obtain, so that the responsibility for the interpretation lay entirely with the describers. After all, if we follow Reason (2003), the performance is what happens in the minds of the audience, in this case, the minds of the describers. The describers were aware of this fact, and realised that their description of the AD as well as the AI needed to move beyond describing what could be seen on stage in terms of dance, to give audiences inroads in the experience of watching dance. As a result, the descriptive team knew that subjectivity would be impossible to avoid, and they therefore chose to embrace this approach and aim to “allow audiences to engage with the performance by offering an impression of what the performance is about” (Reviers 2019a). The explanatory and expressive functions in the AI thereby gained in relative importance as compared to the other functions, deviating from the traditional approach to AI.

In brief, both AIs move away from the more traditional approach which focuses on the informative and narrative functions (Van Gool 2012; Reviers and Remael 2013) of the text, to consciously focus on the explanatory and expressive functions, even though this decision was inspired by very different reasons. The next section discusses a few concrete strategies chosen by the team of describers to operationalise these functions.

5.4 AI strategies

For the pre-recorded AI of War and Turpentine, the describer decided to take a holistic approach to structuring the information, instead of dividing the AI into separate clusters of information, as is often the case in traditional AIs (see the clear-cut content elements identified in the AI corpus discussed in Section 4). The pre-recorded AI for War and Turpentine worked towards a model that combines all this information in a chronological walkthrough of the performance. The structure of the AI therefore mirrored the structure of the performance itself.

This AI, for instance, started with a detailed description of the opening image, i.e., the setting of the first part of the performance. It also included a description of the three actors that play one and the same character and how they relate to each other in the physical space. Arriving at the second part of the performance, the introduction started by explaining the use of text projected on the back wall of the stage. It was important for audiences to know that the way the story is told, here suddenly changes by replacing the narrator by projected words. The AI clarified the function of the text (describing memories from the fields of war) and drew the attention to other signs of the performance that compensated this possible loss of information (visual images, sounds and spoken dialogue later on):

Wanneer er in het verhaal een deel wordt afgesloten, gaat het gordijn omhoog. Op houten panelen achteraan verschijnt de projectie ’14-18’. De vertelling wordt verdergezet in geprojecteerde woorden. Zowel het podium als het licht zijn opengetrokken. Beelden komen nu dichter en harder tot bij het publiek. Geluiden van worstelingen vertellen hier hun eigen verhaal. Soms creëert men de illusie van gevechten, verkrachtingen en lichamelijke uitputting, soms zijn de beelden expliciet.

[After the first part of the story is concluded, the curtain rises. On wooden frames at the back of the stage the projection ‘14-18’ appears. Both the stage and the use of lights have opened up in this second part. The images reach the audience more directly and are harsher. The narration continues to be projected on the screen, while the sounds of struggle tell their own story. Sometimes the illusion of fights, rapes and physical exhaustion is created implicitly, at other times the images are explicit.]

In addition, several interesting local AI strategies were chosen to reflect the concept and visual style of the performance. These strategies were developed in close collaboration with the artistic team of the production.

A first example is the way the AI referred to the actors. The dramaturg of the production provided feedback on the first draft of the AI and noted that the artistic team never uses the term ‘dancers’ when referring to the five actors behind the curtain, which was the term used in the first draft of the AI. Dancing was just one of their many actions on stage. As the source text discussion above indicated, these five actors had a specific role in the narration, even if they are not named characters. Only referring to them as dancers would not do justice to their role in this performance. The artistic team therefore referred to them as ‘performers’, a term which was also included in the final version of the AI.

A second example concerns how to formulate the presence of an actor on stage. A lengthy discussion between the dramaturg and the describer evolved around what seemed an inherent contradiction in the way the performance was staged. On the one hand, the narrator seemed unaware of what happens behind her during the performance, while, on the other, she was very aware of the events depicted behind her, since the dramaturg clarified that those were meant to be a representation of what happens in her head. The events that took place behind her were her memories and her story as it was told to the audience. This created an immediate link between the actress and the action behind her, but never an interaction. Ultimately, this ambiguous relation was phrased as ze laat alles om zich heen gebeuren, ‘she lets everything around her happen’. In Dutch the same verb phrase laten gebeuren is used to signify that ‘she makes it happen’ or ‘allows it to happen’ and ‘she lets it happen’. That way, both the passive and the active status of her position is captured in one sentence.

The above strategies illustrate the describer’s decision to focus on the explanatory and expressive function and on describing the experience rather than the story. It also illustrates that this was made possible by the collaboration with the dramaturg. In traditional approaches such explanations are often shied away from, because of the lack of insight into the maker’s intentions. Finally, it is interesting to note that the describer did not consciously apply the foreshadowing function in this AI, since there was no AD to which to refer. Nevertheless, the text analysis did show the presence of this function, as the examples above do anticipate and refer to events during the performance.

For the AI of Satyagraha, the describers made very different choices in terms of AI strategies. The AI starts with a general introduction to the background and general themes of the production (see excerpt above in 5.2.1) and a brief, traditional description of the setting. This part was followed by a detailed introduction to each of the three parts of the performance. This followed the traditional informative function of AIs (see 4). In terms of structure, however, the describers chose to follow a strategy similar to that of the previous case study, namely to follow the chronology of the performance itself and to combine elements of the setting, characters, actions, dance, dialogue/libretto, and soundscape in a coherent piece of prose. The following three examples illustrate this approach.

The first example shows how the narrative function of the performance was rendered explicit. Given the impressionistic nature of the performance, the describers decided to use its historical background as a framework for structuring the AI. This resulted in the explicit clarification of the narrative thread underlying the performance.

De tweede scène draagt als titel “Tolstoj Farm”. Gandhi bracht een groot deel van zijn leven door in Zuid-Afrika, waar hij opkwam voor de rechten van de grote Indiase minderheid die daar woonde en geconfronteerd werd met racisme. In die tijd wisselde hij intensief ideeën uit met zijn tijdgenoot Tolstoj en hij richtte in 1904 een gemeenschap op naar de ideeën van zijn vriend, namelijk de “Tolstoj Farm.”

[The second scene is entitled “Tolstoy Farm”. Gandhi spent a large part of his life in South Africa, where he defended the rights of the large Indian minority who lived there and were confronted with racism. At that time an intensive exchange of ideas between him and his contemporary Tolstoy also took place. In 1904 Gandhi founded a community based on the ideas of his friend, the “Tolstoy Farm.”]

On stage, the historical/narrative background takes second place to the subjective impressions of the music, dance, costumes and setting. Whether the members of the audience can name the characters and place them in their historical context related to the Tolstoy Farm depends on their individual background knowledge. Here too, the way in which the piece is staged is as important or even more important than what is depicted. However, the decision to explicitly incorporate this background in the AI, was inspired by the need for a tangible framework around which to weave the descriptions, both in the AI and the AD, because of the impressionistic nature of the performance and due to time restrictions during the AD.

The second example concerns the fact that the AI explicitly anticipated sound events during the performance as a foreshadowing strategy, meant to provide audiences with a ‘cue’ to activate the information from the AI and relate it to what they are hearing on stage at a given moment. The describers meant for it to serve as a compensation strategy, aiming to direct the audiences’ attention to what they could hear and what that meant, rather than to what they could not see. With this strategy, the describers hoped to enhance the other active senses of their audience and stimulate them to focus on the soundscape of the performance, enhancing their experience of the performance, rather than their knowledge of what was physically happening on stage:

Wanneer een eenzame contrabas inzet, betreden één voor één enkele dansers in lichte, blauwe kleding het podium. Ze dansen kris kras door elkaar, elk hun eigen stuk, maar maken wel dezelfde stijl van bewegingen die doorheen de voorstelling steeds opnieuw terugkomen en voortdurend herhaald worden.

[When a solitary double bass sets in, one by one, dancers in light blue clothing enter the stage. They dance in crisscross, each one of them performing their own piece, but using a similar style of movement, that is repeated over and over again throughout the entire performance.]

The third and final example relates to the foreshadowing strategies of the dance sequences. The AI of Satyagraha already included quite extensive descriptions of dance sequences, as a foreshadowing strategy. These descriptions were meant to compensate for the lack of description during the performance due to time constraints, but also to acquaint listeners with the style of the choreography, describe recurring movements and introduce the terminology that would be used in the AD (see Fryer 2009).

Zo draaien de dansers voortdurend om hun as, behendig manoeuvrerend tussen de in het duister bijna onzichtbare kabels waaraan het platform hangt. Hun armen schetsen onophoudelijk cirkels rond hun hoofd en romp, de ene keer met de handpalmen op elkaar, als in gebed, de andere keer haken ze hun duimen in elkaar en fladderen met hun handen als vleugels van een vlinder of vormen hun handen de openbloeiende bladeren van een lotusbloem.

[The dancers are constantly rotating around their axis, skilfully manoeuvring between the almost invisible cables with which the platform on which they are dancing is attached to the ceiling. Their arms constantly draw circles around their heads and torso, sometimes with the palms of their hands touching, as if in prayer. At other times they hook their thumbs together and flutter with their hands and fingers suggesting the wings of a butterfly or their hands form the blossoming leaves of a lotus flower]

The strategies above illustrate the dominance of the explanatory function. The AI included information that the dramaturgical materials or paratexts provided, introducing additional information layers. As such, the AI provided audiences with inroads into the performance, as a compensation strategy, if you will, for the lack of a traditionally pre-prepared AD and AI.

However, the use of such explanatory strategies actively guides the point of view of the audience towards a certain interpretation, just like the camera in films determines the viewers’ perspective on events. In theatre, point of view is to a certain extent more an individual choice, that of every single audience member. The director can attempt to guide the audience’s attention with music, set design, lights, or indicate what is the focus of the attention of the actors at a given time, the final decision, however, remains with the individual audience members. This is also the case for patrons with sight loss. Ideally, a describer avoids descriptions that are very explicit about what the audience members should focus their attention on. The description does not need to explain how audience members should interpret their aural experience.

However, in the case of Satyagraha the more extensive mediating role of the describers and their responsibility in guiding the audience’s point of view is evident, and was inspired by the specific context. The extent to which such a subjective approach is appropriate for AI has always been and will always remain a point of debate in the field. The AI for War and Turpentine, was different in this respect, because it does not prioritise any of the different narrative layers created on the stage, so that the audience can move freely between them as they are watching or listening to the performance. The question of what gives value to a performance – knowing its background or experiencing the first impression allowing for individual interpretation – is one which describers have to carefully offset against the context of each individual performance. Particularly for contemporary theatre AI, where the input of the audience in the interpretation of a performance’s meaning is so central.

Against this background, however, it is important to keep in mind one of the key differences between the case studies, namely the fact that the AI for War and Turpentine was not created in combination with an AD. While both AIs focused on the explanatory and expressive functions, the way these functions were operationalised in the local AI strategies was very different. Most decisions in the AI of Satyagraha were directly and consciously impacted by the need to foreshadow, due to the acute lack of time during the performance for AD. This factor did not play a role at all in War and Turpentine.

What both case studies do illustrate is the texts’ tendency to shift from the predominantly informative function of AIs to their role in conveying the narrative, visual and dramaturgic style of a performance, as mentioned in the Introduction. In this way they aim to help audiences experience the performance rather than merely ‘understand’ what is visible or happening on stage. The discussion, however, showed that operationalising the expressive and explanatory function of AIs is difficult, because it requires a thorough understanding of the many explicit and/or implicit layers of meaning in a performance, increasing the need to interpret and potentially (but not necessarily) limit the ways in which the audience (re)creates the performance in their minds. Moreover, many different factors impact on the strategies chosen by describers to operationalise the appropriate functions: the type of AI, the type of target audience, the creation process, the input from the artistic team, practical considerations such as planning and rehearsal time, but also the specificities of the performance, its multisemiotic nature, genre, languages spoken, etc. The discussion confirms Fryer’s statement, that “ADs and AIs for the performing arts are all about tailor-made solutions, adapting the chosen strategy to fit the requirements of the context of a given situation” (Fryer 2019).

6. Conclusion

The present article explored the creation process of AIs to gain a better understanding of the possible functions they can fulfil in different communicative situations and to identify some of the strategies audio describers develop to express these functions. However, a clear distinction between functions cannot always be made; the relative weight of these functions can vary greatly from one AI to another and describers develop varying strategies to operationalise them. The present article only touched upon a selection of variables that may shape describers’ decision-making process, based on a specific multilingual corpus and two Dutch case studies. AIs are continuously being (re)designed to fit the requirements of new communicative situations, giving rise to additional variables that make up a complex web of parameters shaping the AI creation process more generally.

This observation raises questions regarding how to classify AIs. Should AIs be viewed as translations, as a writing process or even as an artistic creation process? Where can we draw the line between the source text, the paratexts surrounding the performance and the AI, and the target text with AD? To what extent can AIs and ADs be seen as paratexts themselves, as they become part of the genesis of a performance, and the performance itself? This conflation of textual variance and translation, of source and target texts, shows easy classification is difficult as AIs seem to merge varying semiotic and transformative acts into one.

These observations resonate with similar observations in other subfields of Translation Studies – such as literary translation, Interpreting Studies, museum translation, to name just a few. This explains the recent interest of Translation Studies and Media Accessibility in Complexity Thinking, which conceptualises translations as complex phenomena in which large networks of human and non-human variables interact with no central control, giving rise to ever-changing phenomena that cannot be explained by a set of clear-cut rules or concepts (see, for instance, Remael et al. 2019; Remael and Reviers 2019; Reviers 2019b; Marais and Meylaerts 2019). More specifically, Tymoczko (2019: 240) argues that translation can be seen as a form of organised complexity, i.e., “dealing simultaneously with a sizeable number of factors which are interrelated into an organic whole.” While translation is indeed shaped by a high number of interrelated parameters, it is not a random process either. Indeed, the functional approach taken in this article highlights the purposeful nature of the creation process of AIs and the key role describers play in creating what Tymoczko calls an ‘organic whole’.

Two key concepts of complexity thinking in Translation Studies, in particular, have surfaced implicitly in the present article, namely the nonlinear and generative nature of AI processes. The notion of nonlinearity refers to the fact that the whole is more than the sum of its parts. The interaction of variables in the translation process can only be understood in a holistic way, not in linear terms of action and reaction or as a linear movement from source to target text (Marais and Meylaerts 2019). This clearly resonates with both case studies in Section 5.

Second, the AIs under study not only transformed an existing message into a new form, but generated additional layers of meaning for the performance and its paratexts. This observation echoes Tymoczko’s (2019: 246) argument that translation is a generative process: “Generativity is a mainspring of the complexity of language and also a central feature of the complexity of translation.”

The meaning-generating and unstable nature of both AIs stands out in our case studies but is present to different degrees in all forms of translation. Given the presence of new text types in a practice that is often ahead of research, the tendency of some ‘practisearchers’ to be prescriptive and produce AI or AD guidelines is understandable. Translation Studies more generally also had to mature to lose its prescriptive streak. However, given the complexity of the theatrical contexts in which AIs (and ADs) are written today and the myriad of functions they can be expected to fulfil, writing comprehensive guidelines seems a futile undertaking at this stage. AIs and ADs are translations; in our view, that means complex semiotic processes that go well beyond what is traditionally considered to be ‘translation’. We therefore see both text forms as concrete examples of what scholars such as Marais (2018) consider to be translation, from the perspective of Complexity Studies. As the instability of source and target texts is becoming increasingly apparent in many (multimodal or semiotic) contexts, many scholars are increasingly studying the agents and actants shaping the translation process next to the translation product itself, which is always in the course of being shaped in users’ or spectators’ minds. This study is therefore in many ways a pilot study of a text genre that deserves much more research and which would certainly benefit from including the role of the users as co-creators of texts, in our case, the audiences actively making and shaping the meaning of theatrical performances. Nevertheless, the present article hopes to have illustrated the complex and interdisciplinary nature of AIs, in order to initiate further academic exploration of this text genre as a form of translation that defies easy classification.

Acknowledgements

This paper is based on the PhD project of Hanne Roofthooft (2017-2021) of the University of Antwerp, Departments of Applied Linguistics/Translators and Interpreters and Theatre, Film and Literature Studies. Supervised by Kurt Vanhoutte, Aline Remael, Luk Van den Dries, the project is funded by the BOF research fund of the University of Antwerp. The paper is also indebted to the Master’s thesis written by Selina Van Gool at the University of Antwerp, Department of Applied Linguistics, Translators and Interpreters in 2012, under the supervision of Nina Reviers and Aline Remael. We would like to thank Toneelhuis Antwerp, Needcompany, Opera Ballet Vlaanderen, and all the describers, theatre professionals, and visually impaired users who have contributed to the successful completion of the above projects.

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  • York, Greg (2007). “Verdi made visible: audio introduction for opera and ballet.” Jorge Díaz Cintas, Pilar Orero, and Aline Remael (eds) (2007). Media for All: Subtitling for the Deaf, Audio Description, and Sign Language. Amsterdam/New York: Rodopi, 215-229.
    • Websites
  • Audio Description Projects (no date) http://www.adlabproject.eu/ (consulted 15.11.2020)
    • Performances
  • Cassiers, Guy (2011). Bloed en rozen. Premiered in May 2011, Antwerp, Belgium.
  • Cherkaoui, Sidi Larbi (2017). Satyagraha. Premiered in April 2017, Basel, Switzerland.
  • Lauwers, Jan/Needcompany (2017). Oorlog en terpentijn. Premiered in December 2017, Antwerp, Belgium.
  • Olympique Dramatique (2012). Van de Velde, J’aimerais Mieux de bouche vous le dire [sic]. Premiered in March 2012, Antwerp, Belgium.
Biographies

Reviers PortraitNina Reviers (TricS research group) holds an MA in Translation and Interpreting and a PhD in Translation Studies from the University of Antwerp. She is a lecturer and researcher in the field of Media Accessibility at the University of Antwerp and manager of the university’s newly founded Expertise Centre for Accessible Media and Culture OPEN. She is an Editorial Board Member of the Journal of Audiovisual Translation and Steering Committee member for Languages and the Media.


Email: Nina.reviers@uantwerpen.be

Roofthooft PortraitHanne Roofthooft started a one-year pilot study in August 2015 on audio description for post-dramatic theatre financed by the University of Antwerp. A follow-up study started in 2017 and focuses on audio description and audio introduction for contemporary theatre. She is currently preparing a PhD at the Departments of Theatre, Film and Literature Studies and Applied Linguistics/Translators and Interpreters. Hanne received her Master’s degree in Film and Theatre Studies from the University of Antwerp in 2014. Her dissertation focused on the use and usefulness of semiotics for Theatre Studies.


Email: Hanne.roofthooft@uantwerpen.be

Remael PortraitAline Remael (TricS research group) is Professor of Translation Theory, Interpreting and Audiovisual Translation/Media Accessibility at the Department of Applied Linguistics/Translators and Interpreters of the University of Antwerp. Her main research interests and publications are in AVT/Media Accessibility, including audio description (AD), and live subtitling with speech recognition. She is currently a partner in two European Erasmus+ accessibility projects (ADLAB PRO and ILSA) and is the co-founder of OPEN, the UAntwerp’s new ‘Expertise Centre for Accessible Media and Culture’. In 2018 she received the ESIST Jan Ivarsson Award for invaluable services to the field of audiovisual translation.


Email: Aline.remael@uantwerpen.be

Notes

Note 1:
ADLAB (Audio Description: Lifelong Access for the Blind) was a 4-year project funded by the European Union, conducted between 2011 and 2014. See Audio Description Projects (n.d.) for more information.Return to this point in the text

Note 2:
An extensive analysis of this other AI to War and Turpentineis beyond the scope of the present article. In particular, this AI followed the traditional, predominantly informative and factual approach to AI (see Section 4). The analysis in this article focuses on examining newer approaches, representative of the proliferation of varying approaches to AI. More about the traditional approach can be found in Van Gool (2012) and the ADLAB AD guidelines (Remael et al. 2014).Return to this point in the text