Designing a Multimodal Corpus of ELF in ESP Sustainable Data Collection and Analysis Chiara Barbagianni, Valentina Baselli, Clara Pignataro
Data Collection in the Interpreting Classroom
ELF in Interpreting In the literature, there is some evidence that interpreting from ELF conference speakers is more demanding than interpreting from native speakers of English in terms of cognitive load (AlblMikasa 2010; 2013a). This is mainly due to the lack of pragmatic fluency, pronunciation problems and the use of other unconventional language structures (Albl-Mikasa 2013a; Albl-Mikasa 2013b) and evidenced by an empirically observed deterioration in interpreters’ performance. This additional processing cost when interpreting from non-native speakers is reported to deteriorate the interpreter’s performance in terms of source-text comprehension, analysis, shortterm memory management and target text production (Gile 1999; 2005; Albl-Mikasa 2010). This is particularly acknowledged in Language for Specific Purposes (LSP) conferences where a high degree of accuracy and precision is required (see Gile 1995).
Preparation phase
Sight translation and paraphrasing exercises
Week 1
Ultrasound surgery (Speaker from Israel) + Data collection Terminology test
Preparation phase
Terminology work (Human cancer)
Week 2
Human cancer (Speaker from Iran) Cloze test - Brainstorming on terminology - Interpreting strategies discussion
Preparation phase
Terminology work (Neuroscience)
Week 3
Neuroscience (Speaker from France) Cloze test - Brainstorming on terminology - Interpreting strategies discussion
Effectivity balances the textual level with the extra textual one. From the standpoint of training, this means an enhancement of both encyclopedic and domain specific knowledge and a consequent use of LSP in the target text delivery.
Preparation phase
Terminology work (Technologies to cure cancer)
Week 4
Technologies to cure cancer (Speaker from Mexico) Cloze test - Brainstorming on terminology - Interpreting strategies discussion
Efficiency involves primarily the extra textual level. After performing an ad hoc terminology
Preparation phase
Terminology work (Review)
work, the interpreter can better manage cognitive strength by allocating more cognitive resources on the Production Effort (Gile 2005) and providing for a pragmatically sound target text delivery.
Week 5
Ultrasound surgery (Speaker from Israel) + Data collection Terminology test - End of class survey
Linguistics
NLP
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The ● Data Science Paradigm Computer The Data Science paradigm follows from Conway’s (2010) Venn diagrams, a simple yet sharp schema visually explaining the multidisciplinary nature of the state-of-the-art data driven approaches. This Venn diagram infographic has been increasingly popular in the analytic industry ever since, rapidly gaining the status of an iconic outline also the academia (Azam 2014). The need for crafting an interdisciplinary language-analysis toolkit has been stressed in grey literature sources as well as in scholarly publications (Barbagianni 2013). While Biber and colleagues (1998: 254) argue that careful coding is the ultimate way to perform robust corpus analyses, quantitative and computational techniques start being applied in the newborn field of Digital Humanities (Manovich 2016).
Science
Text Mining
Corpus Linguistics
End of Class Survey Mathematics and Statistics
Here, we propose an original interpretation of Conway’s Venn diagrams as adapted to investigating natural language data. The three neighboring fields are Linguistics, Mathematical Sciences, and Computer Science. The first provides an in-depth understanding of the several mechanisms found in language phenomena, although these descriptive endeavors are still heavily focused on human cognitive processing. This is one of the reasons why linguists generally seem to reject quantitative approaches, which focus instead on gathering sufficient amount of data to analyze discrete (language) behaviors, trying to parametrize them and prove - or disprove - their generalizability. On the other hand, we can’t take full advantage of the mathematical and statistical approach without resorting to some kind of computational application, also considering that processing power and data storage have recently become widely available and increasingly cheaper. The intersection between Mathematical and Computer Sciences for investigating text data differs profoundly from the intersection between Linguistics and Computer Science (Zhai & Massung 2016).
References
Conway, D. (2010) “The Data Science Venn Diagram”. Retrieved from: www.dataists.com (31/5/2016). Albl-Mikasa, M. (2010) “Global English and English as a Lingua Franca (ELF): implications for the Gile, D. (1995) Basic Concepts and Models for Interpreter an Translator Training, Amsterdam: Benjamins. interpreting profession”, Transkom 3(2), 126-148. Albl-Mikasa, M. (2013a) “Teaching Globish: the need for an ELF pedagogy in Interpreter Training”, Gile, D. (1999) “Testing the Effort Model’s Tightrope Hypothesis in simultaneous interpreting: a contribution”, Hermes 23, 153-172. International Journal of Interpreter Education 5(1), 4-16. Albl-Mikasa, M. (2013b) “ELF speakers’ restricted power of expression. Implications for interpret- Gile, D. (2005) “Directionality in conference interpreting: a cognitive view”, in R. Godijns, M. Hindedael (eds) Directionality in Interpreting. The retour or the Native?, Ghent: Communicaers’ processing”, Translation and Interpreting Studies 8(2), 191-210. tion and Cognition, 9-26. Azam, A. (2014) “The First Rule of Data Science”, Berkeley Science Review. Retrieved from: Manovich, L. (2016) “The Science of Culture? Social Computing, Digital Humanities and Cultural www.berkeleysciencereview.com (31/5/2016). Analytics”, Journal of Cultural Analytics. Retrieved from: www.culturalanalytics.org Barbagianni, C. (2013) “Terminological and Terminographical Approaches to Conference Inter(31/5/2016). preting”. Poster presented ad EIRSS 2013 (Edinburgh Interpreting Summer School. Edinburgh, Zhai, C.X., Massung, S. (2016) Text Data Management and Analysis. A Practical Introduction to 24-28 June 2013). DOI: 10.13140/2.1.3727.2961 Information Retrieval and Text Mining, San Francisco: Morgan & Claypool. DOI: Biber, D., Conrad, S., Reppen, R. (1998) Corpus Linguistics. Investigating Language Structure and 10.1145/2915031 Use, Cambridge: CUP.
Contacts Chiara Barbagianni SSML Adriano Macagno - Cuneo, Italy Università degli Studi di Genova - Genoa, Italy
[email protected] Valentina Baselli IULM - Milan, Italy
[email protected] Clara Pignataro IULM - Milan, Italy
[email protected]
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