10.6092/issn.1974-4382/15557
Audiovisual translation has long struggled to strike a balance between corpus-based analysis of large amounts of text and the need to systematically integrate multimodality in its research scope, in order to fully acknowledge the complex nature of the audiovisual product. This paper aims to relate on the experimentation that is currently being conducted at Ca’ Foscari University of Venice, with the collaboration of the University of Basel (Switzerland): using an existing software (created for pragmatics) for audiovisual translation. The main aim is to make sure this software is able to support the researcher in transcribing, annotating, adding metadata, managing and querying text and video files. The experimented software has never been used for parallel aligned audiovisual text so far. The contribution here presented first briefly describes a framework developed to analyse language variation and multimodality (with a focus on character design) in audiovisual translation, which was then immersed in the software. Subsequently, the software itself is described in detail, with specific attention to its potential and limits in the use within the field of Translation Studies and audiovisual translation. This is done by showing examples from a pilot study that belongs to a broader corpus currently under construction.
application/pdf
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Dora Renna
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linguistic variation
Methodological Challenges in Audiovisual Translation: Experimenting New Software for Multimodal Corpus-based Analysis
Text.Serial.Journal
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34
Articles
2022-09-22
2022-09-21
2022-09-22
2022-09-22
A199-A219
M
Dora Renna
2022/09/22
10.6092/issn.1974-4382/15557
A199
Università Ca' Foscari Venezia
1974-4382
linguistic variation
en
A219
Methodological Challenges in Audiovisual Translation: Experimenting New Software for Multimodal Corpus-based Analysis
https://mediazioni.unibo.it/article/download/15557/14728
34
Updating Discourse/s on Method/s                                       mediAzioni 34 (2022)
                                                                             ISSN   1974-4382


     METHODOLOGICAL CHALLENGES IN AUDIOVISUAL
   TRANSLATION: EXPERIMENTING NEW SOFTWARE FOR
        MULTIMODAL CORPUS-BASED ANALYSIS
                                            DORA RENNA
                                  CA' FOSCARI UNIVERSITY OF VENICE

dora.renna@unive.it



Citation: Renna, Dora (2022) “Methodological Challenges in Audiovisual Translation:
Experimenting New Software for Multimodal Corpus-based Analysis”, in Lucia
Abbamonte and Flavia Cavaliere (eds.) Updating Discourse/s on Method/s, mediAzioni 34:
A199-A219, https://doi.org/10.6092/issn.1974-4382/15557, ISSN 1974-4382.



Abstract: Audiovisual translation has long struggled to strike a balance between corpus-
based analysis of large amounts of text and the need to systematically integrate
multimodality in its research scope, in order to fully acknowledge the complex nature
of the audiovisual product. This paper aims to relate on the experimentation that is
currently being conducted at Ca’ Foscari University of Venice, with the collaboration of
the University of Basel (Switzerland): using an existing software (created for pragmatics)
for audiovisual translation. The main aim is to make sure this software is able to support
the researcher in transcribing, annotating, adding metadata, managing and querying
text and video files. The experimented software has never been used for parallel aligned
audiovisual text so far. The contribution here presented first briefly describes a
framework developed to analyse language variation and multimodality (with a focus on
character design) in audiovisual translation, which was then immersed in the software.
Subsequently, the software itself is described in detail, with specific attention to its
potential and limits in the use within the field of Translation Studies and audiovisual
translation. This is done by showing examples from a pilot study that belongs to a
broader corpus currently under construction.

Keywords: audiovisual translation; digital humanities; multimodality; linguistic
variation.




Copyright © 2022
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1. Introduction

By their own nature, audiovisual products “involve the simultaneous deployment
of several sign repertoires (including but not limited to speech or the written
language of subtitles, film editing, image, music, colour or perspective)”,
perceived by the audience simultaneously through “the synchronized use of
multiple media” (Pérez-González 2014: 186-187). This is the essence of
multimodality, intended both as “the combination of speaking, writing,
visualization and music” (Pérez-González 2014: 185) and the investigation of
these modes and the way(s) they combine (Bateman 2014: 6). Multimodality is
the distinguishing feature of audiovisual texts, whose meaning goes beyond the
sum of its parts, since each part can complete, explain, multiply, and modulate
the others (Lemke 1998: 92). Here lies the fascination of audiovisual products,
as well as their complexity. There is a stark contradiction between the naturally
holistic fruition of such products and the difficulties of dealing with them when
it comes to analysis – which, by definition, requires to set things apart in order
to understand them. The result is that “‘texts’ and ‘images’ are generally
addressed by very different groups of disciplines and those disciplines often take
different views both on just what they are describing and how descriptions
should be pursued” (Bateman 2014: 5). Translation Studies (TS) and in particular
the field of Audiovisual Translation (AVT) have long suffered from this
separation (Chaume 2004; Gambier 2006; Díaz-Cintas 2008). While
multimodality can successfully be integrated in single case studies, the attempt
to do so with a descriptive, corpus-based approach seems to pose even greater
challenges (Ramos Pinto and Mubaraki 2020). The reason should probably be
found in the mainly text-focused tools that are available to researchers – bearing
in mind that tools like media, languages and technology work as extensions of
human mind and culture (McLuhan 1964).
     Multimodality becomes perhaps even more central when translation scholars
and practitioners are faced with the delicate issue of on-screen representation of
minority groups in films. Here, in fact, language and non-verbal aspects of an
audiovisual product work in synergy and can become very influential, given the
power of cinema to shape the collective memory (Fluck 2003: 224). The issue of
mediated images of minority groups within a society has been dealt with in
various fields, including gender studies and media studies (e.g. controlling images,
Hill Collins 2002; poetics and semiotics of stereotyping, Ramírez Berg 2002) and
has recently been acknowledged by TS with the study of imagology (van
Doorslaer et al. 2016). Like the name of the discipline itself suggests, this type
of study would immensely benefit from a multimodal approach. The need to
integrate qualitative single-case studies with broader corpus-based studies lies in
the descriptiveness that is typical of corpora. Descriptiveness in TS helps
overcome the assessment perspective, which is certainly useful but may, in some
cases, hinder understanding and contextualisation (Laviosa 2002: 14).
     That is why this article sets out to contribute to a shift in AVT studies by
relating on the experimentation currently being conducted at Ca’ Foscari
University of Venice: using the software EXMARaLDA (Extensible Markup
Language for Discourse Annotation) for audiovisual translation. The software,
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born for analysing oral corpora and broadly used in pragmatics, had never been
experimented in the field of AVT. Such an effort was possible thanks to the
precious collaboration and training provided by one of the main software
developers, Thomas Schmidt (University of Basel, Switzerland), with the support
of the Department of Linguistics and Comparative Cultural Studies (Ca’ Foscari
University of Venice). Section 2 provides an outline of a framework designed to
analyse language variation and character design in AVT with a multimodal,
corpus-based approach. Most space is dedicated to Section 3, where the main
features of the software, their relevance to the framework at hand and to TS
scholars in general are explored in detail. Finally, some conclusions concerning
methodology are briefly collected in the last section.


2. A framework to analyse language variation and character design with a
corpus-based multimodal approach in AVT

According to Ramos Pinto and Mubaraki (2020), the focus of AVT on the written
mode is especially apparent in corpus-based research (today particularly
appreciated by TS scholars). Corpora techniques (and technologies), however,
have mainly focused on textual analysis. The authors note that audiovisual
translation studies tend to focus either exclusively on text in order to analyse large
corpora, or to use qualitative (and smaller) case studies to look at a broader range
of aspects of the audiovisual product. The construction and even more the
processing of a multimodal corpus is a particularly demanding task, as the
objective should not be to just store the audiovisual material, but also to make it
analysable and processable (Allwood 2008: 208). In this sense, Iedema (2003: 48)
stresses a key point in multimodal studies, namely that the focus should be “the
relationships between […] different semiotics, and on the “division of labour”
between them in particular representations”. This alludes to the importance of
building a bridge between different modes, using relationships as linchpins. Ramos
Pinto (2018) and Ramos Pinto and Mubaraki (2020) elaborated this concept, also
informed by the multimodal work of Pastra (2008). Their breakthrough work is a
key reference in the development of the framework presented here.
    The model consists of three dimensions (Ramos Pinto 2018; Ramos Pinto and
Mubaraki 2020; Renna 2021). The first is the textual dimension, focused on
linguistic variation and its realisation. The second is the diegetic dimension,
focusing on the intermodal relations established between textual dimension and a
selection of cinematic elements that are considered relevant for image
construction. The third dimension is the sociocultural one, allowing to interpret
both source text (ST) and target text (TT) in the light of the context that generated
them. Since the first two dimensions are the most quantitative and product-
based,1 they were at the core of the software experimentation and will be
explored here.



1
 The third one is conceived as the final step of the analysis, and will involve perception studies
aimed at verifying the product-based findings.
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     The aim is to show how linguistic variation and multimodality interact in
the ST to outline the mediated image of a minority, or ethnotype (van Doorslaer
et al. 2016),2 and how such interactions are (re)presented in the TT. While the
first experimentation was carried out on a synchronic case study with manual
tagging and two different data processing software for statistic elaboration
(Renna 2021), the current experimentation has been conducted on a pilot study
that is part of a larger research project at Ca’ Foscari University of Venice. The
project is a diachronic study of the cinematic image of characters of Chinese3
origin in Hollywood films and Italian dubbing.4 In particular, the pilot study was
the film Charlie Chan in London (1934) and its Italian dubbed version, Il nemico
invisibile, as this was the oldest film in the corpus and had some crucial features
for the analysis. The protagonist is Charlie Chan, a character notoriously
speaking a strongly stereotyped variety of English and using an array of
stereotyped mannerisms (Huang 2010; Ma 2014). Moreover, since the film is
rather short (less than 80 minutes) and Chan is the only character of Chinese
origin in the film, the relatively limited number of minutes to analyse seemed
ideal for a pilot.
     The framework is designed to answer the following questions: 1) what
fictional variety does the character speak in ST and TT? 2) which marked
features characterise this variety? 3) which strategies were adopted in dubbing?
4) how do intermodal relations contribute to meaning making in ST and TT?
Since the study focuses on character design, only the relevant character is
sampled out of the whole film. The unit of analysis chosen for dubbing is the
line, spotted according to cinematic conventions such as turn taking (also
signalled though body language), long interruption (usually >3 seconds) with
new informative material being introduced, or scene change (Gurskis 2006: 180).
     Once the text has been transcribed and divided into lines, the textual
dimension analysis requires to attribute each line to a fictional linguistic variety.
It is important to underline that in fiction the language is not a realistic
representation of spontaneous speech, but rather “the creative use of linguistic
varieties” used as “a textual resource that helps the reader to define the
sociocultural outline of the character, as well as his/her position in the
sociocultural fictional context” (Ramos Pinto 2009: 291). This implies that real-
life taxonomies might not be satisfactory when dealing with fiction, and that is
why the classification applied to this pilot is designed for fictional variation
(Renna 2021) and based on two main criteria: 1) belonging to a group considered
2
  The study does not focus on the diasporic identity of the characters, but on their mediated
images or ethnotypes, intended as “rationalizations of cultural difference” (van Doorslaer et al.
2016: 3).
3
  Since, as previously stated, the analysis focuses not on identity but on ethnotypes, it might be
important to define here some key concepts concerning terminology. The terms “Chinese
American” and “Chinese diaspora” are referred to overseas Chinese (Huáqiáo, 华侨), living in the
United States, as well as to American citizens of Chinese origin. The word “Chinese” refers here
to all ethnic Chinese. While a deep debate on identity is certainly important, it is not the aim of
this methodological article. For further reding on the matter, some crucial readings (that I used
as a reference in my studies) are Lisa Lowe (1991), Yến Lê Espiritu (1992), or Peter Feng (2002).
4
  The project title is A Multimodal Study on the Construction and Perception of Linguistic Variation
and Stereotypes in Audiovisual Translation, and it is being carried out at the Department of
Linguistics and Comparative Cultural Studies of Ca’ Foscari University of Venice under the
supervision of Francesca Santulli, Linda Rossato, and Elisa Bordin.
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either as dominant or minority within the diegetic context considered (in the pilot
study, White Anglo-Saxon Protestants or people of Chinese origin respectively);
2) adherence to what is considered as the “standard” variety in that fictional
world. In this case study, the ST standard is the mid-Atlantic accent, particularly
appreciated in 1930s cinema (Naremore 1988: 49). The standard in the Italian
TT is dubbese, a fictional variety closer to written and not used by speakers
outside of dubbing (Antonini 2008: 136). These two criteria define the prestige
of a variety: the more a character occupies a central position in terms of
language, the more prestigious their sociocultural positioning (Ramos Pinto and
Mubaraki 2020). The scheme used for the classification is the following:




                      Figure 1. Linguistic variation in films (Renna 2021)

Each line can only be attributed to one option (other language is the option for
code switching to heritage language, not used in this pilot). Subsequently, the
marked elements are identified – more options can be selected – phonetics (and
suprasegmental traits), morphosyntax and/or lexicon. The query phase with
EXMARaLDA suggested adding other linguistic categories: 1) discourse, intended
as verbal communication features attributable to an ethnotype; 2) proverbiality
(utterances recalling chéngyǔ成语 and proverbs, be it real Chinese sayings or
made-up ones); 3) qǐng-like calques from Mandarin (请).
     The diegetic dimension sheds light on the intermodal relations between
textual dimension and the broader audiovisual context. Conceiving multimodality
in terms of relation (rather than, for example, description) allows a
straightforward data elaboration, easily manageable by individual scholars or
small groups (Ramos Pinto and Mubaraki 2020). However, while a film is a
complex unit of different elements perceived and processed simultaneously by the
audience, to analyse means to isolate the most relevant parts of the audiovisual
whole. Drawing from by Ramírez Berg’s poetics of stereotyping (2002), the selected
elements are: 1) costumes and make up, the latter also comprising scene make up

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(e.g. dark circles for a tired look); 2) phenotype (either real or ostensible) that
hints at the belonging to a certain group; 3) behaviour, the way a character marks
their presence on the scene; 4) interlocutors, i.e. the other characters participating
in the interaction; 5) setting, including a series of cinematic features like mise-en-
scene, framing and lighting; 6) music, both intended as soundtrack and music
played in the scene. During the query phase, a need to focus on the character’s
proxemic behaviours emerged, and a category regarding “proxemics” was added
to the analysis. The range of possible relations is drawn from Ramos Pinto and
Mubaraki’s (2020) adaptation of Pastra’s (2008) scheme to AVT studies. The main
ones are confirmation and contradiction, based on whether the linguistic variety
used is expected within the diegetic context. Confirmation can be further divided
between equivalence or complementarity, depending on whether the verbal and
non-verbal aspects overlap or complete each other. For each line only one option
is chosen for each of the non-verbal elements.


3. A multimodal software for a multimodal analysis: using EXMARaLDA in AVT

EXMARaLDA (Extensible Markup Language for Discourse Annotation) was chosen
for the experimentation whose aim was to join the various aspects of the complex
framework here presented in one software environment, while keeping both
language variation and multimodality at the core of my AVT studies. EXMARaLDA
is a software with a strong multimodal vocation, which allows working with corpora
of spoken language. It was conceived with multiple aims. First, to streamline the
exchange of spoken language corpora among different technological environments;
second, to use multimedia and hypertext in language analysis; finally, to host large
and reusable corpora (Schmidt and Wörner 2009: 565-566).
     The software includes three main tools,5 each dedicated to a specific phase of
corpus-based analysis:

      ● Partitur Editor, the tool for transcription,6 editing, annotation and output
        of multimodal corpora;
      ● Corpus Manager (CoMa), for corpus management and organisation,
        metadata entry, corpus bundling and subdivision;
      ● Exakt, the corpus analysis and concordance tool, which allows to query
        and quantify data, as well as to export results.

These three tools are interdependent, as they work like phases of an assembly line,
where each delivers a semifinished product to be further elaborated inside the
next. Developed starting from 2000 at the Research Centre for Multilingualism in
Hamburg, EXMARaLDA was used for a series of spoken corpora. The software
website7 contains downloads for different operating systems (regularly updated),
5
  There are also other tools: FOLKER, a simplified version of Partitur Editor optimised for spoken
German, and OrthoNormal, for normalisation of FOLKER transcripts. Since they are especially
dedicated to German, they will not be analysed in detail in this article.
6
  EXMARaLDA is suitable for different transcription conventions and features a keyboard for
special characters.
7
  Available in English and German: https://exmaralda.org/ (visited 19/07/2021).
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guides and video tutorials (in English or German), as well as a list of publications
about EXMARaLDA and its applications. It also contains an email address to
contact the developers for any queries.
     Probably the previous most relevant experiences to the present study are the
EXMARaLDA-based corpora in the fields of pragmatics, bilingualism,
interpreting and written-text translations (Hedeland et al. 2011: 230-232). While
each of these corpora had something in common with the corpus at hand, the
software had never been tested with AVT studies before. There are programs that
can be compared to EXMARaLDA, and that were also taken into consideration
for this corpus. In particular, ELAN (designed for sign language and endangered
languages) and PRAAT (specialised in phonetics) have similar functions.8 The
software here presented can communicate with both, as it is possible to import
whole ELAN an PRAAT corpora into EXMARaLDA and vice versa. One of the
strongest advantages of EXMARaLDA, however, lays in the three-tool structure,
which makes each function particularly powerful and suitable for larger corpora.
     While one may argue that, from a scholarly point of view, audiovisual
translation and pragmatics (the main subject treated with EXMARaLDA) are not
necessarily close fields, from a software perspective there are some common
traits. One is the interest of pragmatics in “linguistic behaviour on different
linguistic levels” (Schmidt and Wörner 2009: 567). The other is the centrality of
context to infer meaning: Schmidt and Wörner (2009: 567) distinguish among
interactional context (provided by behavioural data), situational context (the more
general circumstances in which the event takes place) and ethno-graphic metadata
(background information about the speakers and their social relationships). A
film has all these features, the main difference being its fictional nature.
Fictionality, however, does not make this information any less important: indeed,
as can be evinced from the previous section, they are purposefully laid in the
scene to integrate the communicative meaning of the diegesis.
     It might be worth looking at the tools one by one to explore the way they can
be useful for multimodal and corpus-based audiovisual translation studies. Each
tool is first described, with specific attention to the features that were relevant for
AVT. Subsequently, the outputs and results obtained in each phase are outlined.

3.1. AVT transcription and annotation with Partitur Editor

3.1.1. Description and uses for AVT studies

As a transcription tool, Partitur Editor is based on a musical score outlook,
“which best meets requirements for representing multi-party, multi-level and
multi-modal descriptions of spontaneous interaction” (Schmidt and Wörner
2009: 569) or, like often happens in audiovisual translation, of fictional
interaction that is mostly aimed at seeming spontaneous. It is possible to import
transcripts into Partitur Editor, provided they are plain text files (.txt).


8
  More information on ELAN and PRAAT can be found on their respective webpages,
https://archive.mpi.nl/tla/elan (visited 19/07/2021) and https://www.fon.hum.uva.nl/praat/
(visited 19/07/2021).
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     In the Partitur Editor transcription, different layers extend both horizontally
(in time progression from left to right) and vertically, representing simultaneous
events and/or different analytical aspects of the same event. Each line, called
tier, can be assigned a series of functions, the most important being transcription,
description, and annotation. Tiers are attributed to a speaker. The segment of
speech that is transcribed and annotated in a time section is called an event.
Transcription and annotation are attached to the specific moment they are
referred to, both in terms of time and of position in the video clip, through a
similar process to the one of spotting and input in subtitling. Partitur Editor
features a sophisticated video player9 that allows fine-grain spotting and proves
to be particularly usable throughout transcription and annotation. In addition,
more than one video and audio track can be added to the same transcription file,
which is particularly important for AVT scholars, who usually deal with at least
two versions of the same audiovisual product.
     With regards to my framework, each Partitur Editor file only contains the
transcription of a film sample, represented by one of the characters selected for
the analysis. In the pilot study only one character (Charlie Chan) was analysed.
However, in the other films currently being analysed, more than one character
per film have been selected. Since the focus of my research is character design,
each of them (in both ST and TT version) will be featured in a different
transcription file. This will also allow the file to remain relatively manageable,
given the considerable number of annotation tiers.
     Based on the experience with the pilot study and the dialogue with the
system developer, a good way to achieve alignment is to have ST and TT version
of the same character as two speakers superimposed in the same file, with the
relative annotation tiers following each (see red arrows in Figure 2).




     Figure 2. A capture from the pilot study in Partitur Editor. Media player options circled in
            red, ST and TT version as two aligned speakers marked by red arrows.




9
 The possibility to rewatch the specific scene linked to an event will also come back in Exakt,
keeping the corpus multimodal throughout the process.
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EXMARaLDA, in fact, is also designed for the transcription of overlapping
speakers (be it in the same video and in two different videos). While this may
not be the ideal approach for other types of study, it proved particularly useful
in analysing linguistic and multimodal character design.
    In the capture10 from the pilot in Partitur Editor (Figure 2), it is possible to
visualise the way EXMARaLDA has brought to life the framework presented in
the previous section. To the left, in grey, are the categories (which remain visible
when moving forward in the transcript). CC_EN and CC_IT are the ST and TT
versions of the character Charlie Chan (CC) respectively. The letter [v] represents
the transcription tier, while the others are the levels of analysis of textual and
diegetic dimension. The annotation levels are located right after the
transcription. I used different closed textual options to tag the linguistic variation
and intermodal relation categories (here, MIN SUBST stays for minority
substandard, see Figure 1, while CONF COMP and CONTR for confirmation-
complementarity and contradiction respectively). In the other cases I used
numbers (from 0 to 3) that signalled the absence (0), presence (1) or peculiar
features (2 and 3) of said categories. A file with a complete key to read the
annotated transcription is added as metadata to the CoMa file. Using strictly
codified categories rather than lengthy descriptions allowed speeding up the
annotation process – the descriptions are indeed unnecessary since the software
allows watching the line segment or the entire scene.

3.1.2 Outputs and results

Partitur Editor’s output is the multimodal transcription (extension: .exb, file
type: EXMARaLDA Basic Transcription). Even before reaching the following
steps (CoMa and Exakt), it is possible to output the transcription in different
formats, including HTML (and HTML5 for video clips) for web browsers, RTF
for MS Word files, PDF for printing (Schmidt and Wörner 2009, 570). The
transcription can also be output in the form of a list by saving it as a XLS
stylesheet (Schmidt and Wörner 2009: 571). The transcript can be exported
in similar software like ELAN or PRAAT or saved as a subtitle file. Partitur
Editor is also able to draw a word list based on the transcription, useful to
check for errors but also as a first tool of analysis that may help identify
phenomena that will be explored in detail in the following phases. However,
as will be explained in the following sections, the main use imagined for the
transcriptions is to be organised using CoMa, the corpus management tool,
and then queried using Exakt.
    Overall, the use of Partitur Editor had some important advantages that
can really make the difference for an AVT scholar’s workflow. The most
evident is the multimodal tagging, simple but sophisticated, which will be
particularly comfortable for those who have practiced subtitling. From
spotting to transcription and tagging, the software really supports this
potentially tiresome work, especially thanks to the media player. Rich in

10
  The movie stills from the pilot case study Charlie Chan in London (1934) are covered for
copyright reasons.
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options (see area circled in red in Figure 2) such as loop player, section player,
and even buttons that allow playing the second before or after the beginning
and the end of a section, the media player can be a timesaver for working on
large amount of data.
     The transcriptions of source and target versions can be carried out
separately and then merged automatically in one Partitur Editor file or they
can be carried out simultaneously and then separated manually. In this sense,
it is crucial to have ST and TT videos synchronised, so that spotting (in case of
simultaneous transcriptions) and merging (in case of separated transcriptions)
work smoothly. Furthermore, to make sure the process does not overload the
software, it is important to keep a reasonable number of tiers. In the case of my
framework, the number of tiers of the pilot was rather high, and the merging
process did cause some slowing down. The work on the pilot showed the next
step now being taken for the rest of the corpus: reduce the number of tiers,
assigning more options to the tagging of each tier. For example, the category
discourse is going to include also qǐng-like calques (请) and proverbiality.

3.2. Organisation of the AVT corpus using Corpus Manager

3.2.1. Description and uses for AVT studies

In Corpus Manager (CoMa) the transcriptions are organised together to become
an organic corpus, as it allows to bundle transcriptions (with the respective
multimedia files) and to add a series of metadata on different levels. It is
optimised for bulky corpora. Here, a communication is a whole communicative
event, including all its multimodal elements. The corpus is in fact organised in
communications on the one hand and speakers on the other hand, and it is
possible to associate the two with one another. Indeed, while the same speaker
can be present in more than one communication, each communication usually
has more than one speaker. The metadata entries can be attributed to the whole
corpus, to the communication and/or to the speaker, and they can include all
sorts of relevant information. The metadata list is flexible (can include anything
from attached files, to predefined data, to free text/description), and the
scholar can freely establish the categories.
    As can be seen in Figure 3, on the left, circled in red, are the
communications (at the moment there is only one, but there will be as many
as the transcription files); on the right, circled in red, are the speakers (in this
case, the ST and TT versions of the same character, Charlie Chan). Clicking
on each communication and on each speaker allows to input and see the
metadata. The sub-corpora can be created bundling communications and/or
speakers. When a larger corpus is in place, the list will represent an excellent
visualisation of the whole set of data.




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 Figure 3. An example of speaker metadata in Corpus Manager (red square). Communications
                        (left) and speakers (right) are circled in red.

    One of the possible metadata levels (character) applied to my pilot study can
also be seen in Figure 3 (in the red square). In particular, for Charlie Chan
relevant metadata included information on both the character and the actor
interpreting him. In this case, the Chinese character was interpreted by a Swedish
actor wearing yellowface11 on screen. The language category is “closed”, as the
compiler is asked to select the ISO 639-3 language code (e.g. eng for English, ita
for Italian etc.) The categories in the yellow area (belonging to the description
box) are instead open for the scholar to insert whatever information is deemed
useful. Best practice, however, would be to have those rather standardised too.
In fact, metadata are useful to categorise, but also to stratify the corpus. A solid
and detailed metadata apparatus (with a consistent terminology) will allow
responding to very specific research questions with relative ease. Using the
metadata as filters, the scholar can also operate a selection of a number of
communications/speakers and save them as sub-corpora. In the corpus I am
building, a relevant example is sub-corpora of characters belonging to the same
reference stereotype.12
    With Corpus Manager it is possible to start a corpus from scratch – i.e. an
empty file to be filled with transcriptions – or to automatically generate a corpus
starting from transcriptions with a step-by-step guided procedure. One important
action to carry out once the CoMa file is created is segmentation, an automatic
11
   Nowadays mostly abandoned, yellowface is, much like blackface, a discriminating practice
consisting in onscreen make up aimed at reproducing East Asian features on the face of white
actors, allowing them to play Asian characters. It was a common practice in the 1930s (Luise
Rainer won an academy award donning yellowface in the 1937 film The Good Earth, McFadden
2014). Today it has mostly been replaced by what Ono and Pham (2009) call implicit yellowface,
according to which Asian actors are asked to embody the image WASPs have of them, rather
than their own experience.
12
   The reference stereotypes in this corpus include (but are not limited to) model minority, yellow
peril, FOB (non-integrated immigrant), nerdy/eccentric, invisible (Renna 2021a).
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operation that divides the transcriptions into utterances, words etc. This will
create an additional version of the transcriptions with added information on
tokenisation that will be used by the software itself. It is possible to generate a
list of transcription errors found during software segmentation, so that the
scholar can intervene in the Partitur Editor file to fix the issues. In fact, the error
list is a special .xml file that can be open in Partitur Editor: there, clicking on
each error will take to the place where it was found, simplifying troubleshooting
(see Figure 4).




          Figure 4. Error list generated in Corpus Manager and open in Partitur Editor for
                                           troubleshooting.

This function belongs to the maintenance menu, which contains some crucial CoMa
functions. Since especially bigger corpora will be updated in the course of time, it
is important to stress that Corpus Manager leaves the corpus “open” to updating
and fixing at any moment, and this is all done through the maintenance menu,
containing a series of options for both transcription/annotation and metadata.
This allows automatising tedious aspects of corpus polishing to a great extent.

3.2.2 Outputs and results

The main output delivered by CoMa is the transformation of a series of multimodal
transcriptions, separated and isolated from each other, in a comprehensive,
searchable, updatable, and organised corpus (extension: .coma, file type:
EXMARaLDA corpus). CoMa is where the corpus itself is visible as a whole.
    Even though Exakt (the following step) is the tool specifically dedicated to
querying, CoMa already contains some basic query tools, i.e. wordlist generation
(general and by speaker), general corpus statistics and a reduced Exakt-like
interface featuring part of the queries more extensively carried out in Exakt. In
addition, CoMa allows part-of-speech tagging and querying, as it is linked to the
software TreeTagger13 via the tools menu. The TreeTagger interface will allow

13
     As can be read on its download webpage, TreeTagger is a free “tool for annotating text with
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uploading a parameter file (the working parameters for several languages are
found on the website) and an abbreviation file. The tagging is then carried out
automatically, and the scholar can check and correct the results. Since ST and
TT are usually different languages, the parameter files must be specific for each
language; the best way to use TreeTagger in a bilingual or multilingual corpus is
to split the different languages into sub-corpora and tag them separately. The
part-of-speech tags thus obtained will be available for querying in Exakt.
    It is worth noting that Corpus Manager performs some important
organisational work “behind the scenes”: one example is the aforementioned
segmentation, which allows Exakt queries by automatically generating a “ghost”
transcription. The scholar will not need to open this file: its role is to separate
the parts of the transcription in a way that is recognisable by the software.
    While transcription represents the data input moment, CoMa is the
organising moment, in the same way one can write a series of books and then
place and sort them in shelves based on different criteria, so as to ease the
research of the titles (which is the following step).

3.3. A powerful tool for multimodal querying: using Exakt in AVT

3.3.1. Description and uses for AVT studies

Exakt is EXMARaLDA’s concordance tool. Queries can be carried out on whatever
was included in the Partitur Editor file, of course including both transcriptions
and annotations, as well as on the CoMa metadata and the part-of-speech
tagging, and can combine these various levels. Exakt can be used at any stage of
corpus construction, so that the results can be queried and quantified in itinere.
    The queries in Exakt are carried out through KWIC (keyword in context)
concordance, which will probably be familiar to most scholars who have dealt
with corpus-based research. KWIC queries in Exakt present the values of both
transcriptions and annotations, but also display metadata and annotations in
additional columns if needed. Exakt allows queries with regular expressions
(Stubblebine 2007). It is possible to manually select/deselect and remove the
results, as well as to annotate query results themselves. When a corpus is open in
Exakt, the wordlist and the concordances will also be available (see the boxes to
the left of Figure 4).
    More queries can be carried out at the same time and opened in different tabs
(similarly to browser tabs or MS Excel sheets). In the main query window, the
search bar is at the top, where the scholar decides whether to start the search from
transcriptions or annotations, and the buttons to the right give access to various
options, such as filer application.


part-of-speech and lemma information […] developed by Helmut Schmid in the TC project at
the Institute for Computational Linguistics of the University of Stuttgart”, able to work with a
series of languages, like “German, English, French, Italian, Danish, Swedish, Norwegian, Dutch,
Spanish, Bulgarian, Russian, Portuguese, Galician, Greek, Chinese, Swahili, Slovak, Slovenian,
Latin, Estonian, Polish, Persian, Romanian, Czech, Coptic and old French”. https://www.cis.uni-
muenchen.de/~schmid/tools/TreeTagger/ (visited 19/07/2021). TreeTagger must be
downloaded separately from its dedicated page.
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   To see more about a specific result, it is possible to click on it, and this will
show the Partitur Editor full transcription and annotation, as well as the
multimedia file(s).




                    Figure 5. An example of transcription query with Exakt.

As can be seen in Figure 5, regular expressions were used to search for both ST and
TT version of a word of interest simultaneously. As explained in Renna and Santulli
(2022) the words please and prego were especially recurrent in the pilot study (in ST
and TT respectively). Regular expressions that are useful for the scholar can be
saved in a dedicated library within Exakt, to be easily retrievable for later queries.
    If the transcriptions are merged, scrolling down on the Partitur Editor section
will show both ST and TT versions of the selected line. Various combinations of
transcription, annotation and metadata will be possible, so that the search can be
adjusted and perfected according to the scholar’s needs.




 Figure 6. An example of crossed query in Exakt. The options to add more columns are circled
                                           in red.


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Figure 6 shows how queried words (red), annotations and added analyses
(both green) and metadata (black) are shown simultaneously, along with left
and right context. Even more results can be added to the query. Here a sample
query was carried out to show the interaction of different levels. The main
query was based on the regular expression “(please|prego)”, the same as
Figure 5, but more layers were added. From the menu on the right (circled in
red) I used both the specify metadata (paper clip) and the add analysis functions
(notebook). The chosen metadata was reference stereotype (mentioned in the
previous subsection) – since the pilot study consists in one character, the
result is “model minority” in all cases. The add analysis function is flexible, as
it is possible to add anything between binary options and free text. The sample
analysis added in this case was a closed category list based on the question
“Which option?”, signalling which results corresponded to options deemed
relevant during the query phase (if a certain category is often relevant across
the corpus, the scholar may consider adding it in the transcription). Finally,
using the columns menu (circled in red on the top left), I added one of the
annotation categories, the multimodal relation between speech and
behaviour, so as to visualise how many times the use of the queried words
corresponded to a certain value in that relation, as well as to the reference
stereotype and to the sample added analysis. All these options allow the
scholar to personalise and adjust the query to obtain elaborate results.
      Exakt is arguably what makes EXMARaLDA stand out against competition
the most. The queries are easily carried out, especially for those who are
familiar with regular expressions. All levels of analysis are available for cross-
analysis, which makes the whole process flexible, personalised, and
multimodal. Having a merged transcription (which will correspond to
alignment in this environment) allows the AVT scholar to see transcription,
tagging and multimedia of ST and TT at the same time, at any point of their
research.

3.3.2. Outputs and results

Exakt performs the final step of EXMARaLDA’s work. It does not create tables
and graphs, but allows very sophisticated queries that are visible and
shareable, and can be then saved and retrieved, as well as exported into a
statistics-specialised software like MS Excel (with which many scholars will
probably be familiar) for graph generation. This can be done by saving the
query as a .csv file, so that it can be opened in MS Office Excel to generate
graphs and tables.
    Figure 7 shows an example of Exakt query exported into MS Excel: all the
categories are visible just like in Exakt (see Figure 6), and are ready for
statistical elaboration and graph generation.




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                     Figure 7. Query from Figure 6 exported in MS Excel.

     A thorough and detailed exploration of the pilot results and their
sociolinguistic and translational implications is out of the methodological scope
of this article, and can be found in Renna and Santulli (2022). However, it might
be worth briefly relating on the specific results obtained using EXMARaLDA with
this study. Thanks to the work with this software not only was it possible to
answer the research questions (the graphs and tables were generated with MS
Excel), but I was also able to “see” some further verbal and non-verbal
phenomena that did not directly correspond with my research questions.
     The first research question was: what fictional variety does the character
speak in ST and TT? By querying the annotation tier [variety], I obtained a list
of the varieties used by Charlie Chan, and I checked the relevance of the results.
I saved the query and exported it to MS Excel, where I could then calculate
recurrences: in both ST and TT, the highest-frequency variety was minority
substandard, the most peripheral variety in the scheme (see Figure 1), with 73.5%
and 73% respectively. The only difference worth mentioning was that, while in
the TT there was a slight resort to dominant varieties (overall less 26.1% of the
TT lines), ST Charlie Chan always verbally displayed his ethnic belonging. The
following question was the one concerning variety realisation. In this case I
queried three tiers simultaneously: [phonetics], [morphosyntax] and [lexicon]
in both ST and TT, and then I restricted the query to the most recurrent variety
(minority substandard). The query was subsequently exported in MS Excel for
statistical elaboration. Here I noticed a crucial difference: in the ST
morphosyntax and phonetic/suprasegmental traits are almost equally present
(99.4% and 100% respectively), and lexicon appears marked in 35.5 % of the
lines. The TT was significantly less marked, as phonetics dropped to as little as
2.4%, and lexicon decreased to 12.7%, but morphosyntax slightly increased, as
it reached 100%. These data were crucial to answer the third question. While
there are countless translational strategy taxonomies, I have opted for Ramos
Pinto’s (2017), designed for corpus-based study of language variation in AVT.
Since the variety is mostly kept, but with less marked features, it is possible to
infer that the most used strategy was centralisation, which “accounts for the cases
in which the TT presents a lower frequency of nonstandard features (or the

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choice for more prestigious features/variety in relation to the ST) and can thus
be placed closer to the centre of prestige” (Ramos Pinto 2017: 9). The last
question concerned intermodal relations. Here I used Exakt to query all the
relation tiers ([behaviour], [costumes & makeup], [phenotype], [interlocutors],
[setting] and [music]) simultaneously in ST and then in TT, I saved the queries
and exported them into MS Excel. Data showed that in the ST confirmation
relations are prevalent (70%), especially in the categories of phenotype,
costumes and make up, and behaviour. Plot-required contradiction appears with
interlocutors and setting, both representing the British élite. Music was nearly
absent throughout the film and did not have statistical relevance. TT revealed a
certain increase in contradiction (from 30% to 43%), but the
confirmation/contradiction was overall kept.
    As mentioned above, using EXMARaLDA helped me notice more phenomena
than the ones I was looking for a priori. First, thanks to multimodal tagging I
could “see” the single lines repeatedly with ease, and some very specific non-
verbal features appeared recurring, so I decided to add them as transcription
categories. The character has a distinctive and stereotypical proxemic behaviour,
which mainly consisted in bows and joined hands (or both at the same time) and
appeared in 65.9 % of his lines – a non-negligible frequency. This non-verbal
display of exaggerated modesty is accompanied by verbal ones. These were
noticed in Exakt, where I could query some words that seemed recurrent during
transcription and annotation. In particular, among the verbal displays of
modesty, it is worth citing the calque of the Mandarin word “qǐng” (请), which
can be roughly translated with “please” and “prego” in English and Italian
respectively. These two words were queried using regular expressions and turned
out to appear in 17.7% of the ST and 21.2% of the TT lines. Another stereotypical
discursive trait found in Charlie Chan was his proverbiality, especially realised
with the use of similes, noticed during tagging and then queried in Exakt through
a series of keywords such as “like”. Proverbiality appeared in 19.2% of the ST
and 17.7% of the TT lines in various forms: adaptation of real Chinese sayings,
adaptation of chéngyǔ (成语, Chinese sayings composed by four characters), or
made-up proverbs distinguishable thanks to their structure. While the
percentages vary to some extent from ST to TT, in no case they disappear
completely or increase/decrease to the point of doubling/halving– and this
further confirms the inferred centralisation strategy.


4. Concluding remarks

While most scholars acknowledge the importance of multimodality and corpus-
based approach in AVT, not all may decide to experiment with new software,
especially if they come from qualitative approaches or if they already have a
preferred software – albeit a non-multimodal one. This reluctance often depends
on the overwhelming experience to be immersed in a completely new virtual
environment. While approaching new tools can be discouraging and unnerving
at first, many issues can be solved easily by seeking guidance and collaboration


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with software developers and digital humanities experts. Mastering new
techniques is an empowering effort, which pays off with more solid results.
    In this sense, EXMARaLDA has so far proved to be a great tool, as it has been
able to support the high level of complexity of an AVT scholar’s research questions,
which concern two languages simultaneously, as well as multimodality. Indeed,
the software design allowed “the integration of different levels of linguistic
description and different types of context data”, while “permitting a two-way
interaction between the data and its analysis” (Schmidt and Wörner 2009: 580).
Regular software updating and fixing, and the availability of developers for
support and training are also crucial, especially for those scholars who are not
used to working with software.
    There are some things that, during my training, emerged as pivotal for
EXMARaLDA and, in general, for all software use in corpus-based studies. One is
to deeply reflect on one’s workflow: having in mind neat research questions is only
the starting point – then it is necessary to become acquainted with the software,
to fully understand how it can support the work throughout (rather than making
it more complicated or slowing it down). This makes always worth trying a pilot
study before deciding whether the initial choice of software and/or workflow is
the most suitable for the objective. In this sense, the flexibility to adjust to
emerging needs and even start again when necessary are researcher skills that
become especially useful when working with new methodologies. A corollary to a
clear workflow is an orderly management of the corpus material – which must be
clearly organised, carefully stored and constantly backed up. Although this may
seem obvious, it is quite hard to achieve especially as the amount of data increases.
This is both true when working alone and in groups – each condition poses a series
of different challenges linked to time consumption and shared practices.
    The experimentation here described is still ongoing, and more will emerge as
a full corpus comes to existence in EXMARaLDA – both in terms of methodology
and results. I have now worked on similar projects with and without this software
and, despite the initial hurdles of dealing with a completely new tool, there are
significant advantages in shifting to a more digital approach. One is the actual
multimodality of the whole work, from transcription to querying, which is linked
to the features of Partitur Editor. Another is the fact that the features of an
EXMARaLDA-generated corpus correspond with the requirements of my corpus in
many respects, for example the speaker criterion for corpus sorting in Corpus
Manager very well suited the focus on characters of my framework. Moreover,
Exakt queries are easy to perform but at the same time can reach high levels of
sophistication and complexity.
    This work is an attempt to contribute to an increased synergy between
translation studies (especially AVT) and digital humanities, and to point the way
towards a deeper embracing of multimodal and corpus-based approach to AVT.
The focus on character design was chosen because it is an aspect of the analysis
that shows more than others the need to go beyond the textual dimension. In AVT,
resorting to digital humanities can be the way forward towards embracing and
delving into the polysemiotic nature (Chiaro 2008) of the audiovisual product.



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ACKNOWLEDGEMENTS: I would like to thank Thomas Schmidt (University of
Basel, Switzerland) for his patient guidance and training, and the Department of
Linguistics and Comparative Cultural Studies at Ca’ Foscari University of Venice
for the support that made the training possible.


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