1. Redefining computer-assisted interpreting tools
InterpretBank
Redefining computer-assisted interpreting tools
Dr. Claudio Fantinuoli University of Mainz/Germersheim
University of Innsbruck

2. Aims and preliminary thoughts
Contextualizing the interpreting setting in terms of linguistic and extra-linguistic knowledge
The proposed tool: InterpretBank
Academic research on InterpretBank
Final thoughts 2 2

3. Aims and preliminary thoughts
Development of a tool to cover all phases of an interpreting assignment (from preparation to the interpreting act)
Conference interpreting in focus, but most of the things discussed here can be extended to other forms of interpreting
Focus is on highly specialized conferences, both in terms of content and language, mainly technical and scientific subjects
This is for presenting InterpretBank 3 3

4. Interpretation Setting
High fluctuation of topics and clients
Important role of specialized language (LSP)
Knowledge gap between Participants (experts) and Interpreters (laypersons)
This gap concerns both linguistic knowledge, especially related to terminology and phraseology, and domain knowledge, i.e. expertise in a specific topic, information about the speaker, the situational context, etc. 4 4

5. Consequences
To perform successfully, interpreters have to close this knowledge gap
Central role for the success of the interpreting activity:
Preparatory phase
Information retrieval and access 5 5

6. Traditional Preparatory Phase
Dichotomy between linguistic-oriented preparation and domain-oriented preparation
Typical workflow (mainly terminology-oriented)
Getting preparatory documents (PDF + PPT)
(Cross) reading of the documents and terminology extraction
Search for translation candidates in several (online) media
Creation of a multilingual glossary
Memorization of the glossary
Problems: intuitive, decontextualized glossaries, time-consuming, not easy to reuse, etc. 6 6

7. Traditional “live” Information Access
Difficult to implement during the act of interpreting (time constraints)
Less use of “correct” terminology -> other strategies -> cognitive overload -> bad performance
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8. Idea
Possibility to improve preparation and information access with the help of software, i.e.:
Automatize the information retrieval from the abundance of sources on the Web
Integrate the acquisition of language and domain knowledge
Simplify the access to information during interpreting
Make information reusable in time 8

9. Computer-assisted interpreting
Computer-Assisted (or Computer-Aided) Interpreting (CAI) is a form of oral translation wherein a human interpreter makes use of computer software designed to support and facilitate some aspects of the interpreting task with the overall goal to increase quality and productivity. 9 9

10. InterpretBank Documentation and information extraction
Glossary creation and management
Glossary memorization
Glossary access 10

11. 1: Documentation
Semi-automatic collection of domain-related texts (creation of comparable corpora)
Input: key words of the domain
Texts format: PDF and HTML
Text output: XML -> SQLite database 11

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13. 1: “automatic” information extraction
Word frequency (tokens): list of terms ordered by frequency. Exclusion of stopwords
Relevant terminology: term extraction on the basis of morphological and statistical analysis. Exclusion of stopwords, general words, etc. Available for English, German, Italian, …
Collocates of selected word: list of words that are frequently used with the term analyzed 13

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15. 1: “manual” information extraction
Analysis of the extracted terms through “Key Words in Context” to find translations and confirm/discard translation candidates:
Visualization of contexts from domain related texts
Discovery process (one word leads to the next)
Triangulation: terminology-corpus-original documents 15

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17. 2: Glossary creation and management
Unique database with simple interface to create and manage terminology repositories (glossary) with a two-tire categorization system
Interpreter-oriented way to view glossaries
Glossary exchange among colleagues
Up to 5 languages
Pre-configured term entry 17

18. Simple term structure 18

19. Advanced term structure19

20. 2a: Automatic translation
Translation candidates through integration of online repositories, parallel corpora and automatic translation (dict.cc, mymemory, wikiterm, Bing, etc.)
The function:
speeds up the glossary creation process
proposed translations are a «starting point»
possibility to very proposals through corpus analysis 20

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22. 2b: Integration of preparatory texts
Direct access to the reference texts from the glossary
By clicking on a term the user gets a list of occurences of the term in its real context of use
Possibility to triangulate proposed translations with real texts (client documents), web sources, etc.
Aim:
Get accustomed to the language used at the event
Extend the focus of a glossary to real texts 22

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24. 2c: Automatic lookup of definitions
Look up of definitions on the Web for selected terms
Works bilingually (SL and TL)
Possibility to cross-reading both definitions to aquire linguistic and domain knowledge in both working languages. 24

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26. 2d: Term lookup on database online
Instead of looking up single terms in multiple online resources
Opening several online resources in a single web tab with all settings already done 26

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28. 3: Glossary memorization
Easy way to memorize a glossary prior to the event
It shows terms first in the source language and then in the target language
Designed to memorized small glossaries (50 – 100 terms)
Options:
Manual or automatic term visualization (speed adjustable)
Bidirectional
Scuffled ordering of terms to improve memorization 28

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30. 4: Information access (booth)
Interface to access glossary in the booth
Interface is easy and clear
Options:
Static search (fired ENTER key) / dynamic search (autom. firing)
Stopwords exclusion
Bidirectional
Easy way to add terms to a glossary
Customization of the number of information shown in results
If no results in conference glossary search in entire database
Highly customizable search behavior 30

31. I punti esclamativi indicano risultati di ricerca ottenuti apportando delle correzioni31 31

32. Empirical Research: Corpus Preparation
Xu (2015) investigated how the Corpus Driven Interpreter Preparation improves trainee interpreters’ performances.
Results:
test groups had better terminology performance during SI
having higher terminology accuracy scores
and making fewer terminological omissions
this had an impact on the holistic interpreting performance scores which were higher than in the control groups 32

33. Empirical Research: Int. Performance
Gacek (2015) and Biagini (2016) analyzed if the use of InterpretBank could be seen as a disturbing factor in the interpreting process or if it could provide support.
Results:
all testers had a better performance when using the software over than other more traditional solutions. They were able to search and correctly translate a larger amount of technical terms, reducing term omissions at the same time (correctness and completeness) 33

34. Empirical Research: Didactics
Prandi (2015) analyzed the students’ approach to InterpretBank in the booth while interpreting terminology-dense texts
Results:
most testers were able to conduct effective terminology searches (with an average 90% rate of terms correctly identified)
amount of experience in the use of the tool plays a key role in helping students integrate it in their workflow
drawback: tendency of some testers to rely too much on the software, with negative consequences on the performance 34

35. To come… New version (4) coming out in the next months
Exploring new functionalities, for example:
In the booth: if no results are found in database, search on the Web
Integration of voice recognition to automatic queries the terminological database
More empirical research (first PhD now in Mainz)
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