Publication type: Conference other
Type of review: Peer review (abstract)
Title: NMT literacy at the interface of AI and intercultural intelligence
Authors: Ehrensberger-Dow, Maureen
Lehr, Caroline
Delorme Benites, Alice
et. al: No
Conference details: CIUTI Conference 2020 Artificial Intelligence & Intercultural Intelligence. Actions and interactions in translation, interpreting and target contexts, Online, 9-11 December 2020
Issue Date: 11-Dec-2020
Language: English
Subjects: Machine translation literacy
Subject (DDC): 410.285: Computational linguistics
418.02: Translating and interpreting
Abstract: The recent advances in artificial intelligence, natural language processing, and ready access to freely available online tools are raising people's expectations that quality translation is only a click away, with media dramatically citing research reports claiming human parity for neural machine translation (NMT; Hassan et al. 2018). Neglected in this discourse is the sobering reality of the risks and cultural inappropriateness associated with the misleadingly fluent output of some of these systems (cf. Martindale & Carpuat 2018). Within translation studies and among the professional translation community, interest in digital literacy with respect to recent advances in NMT has been growing (e.g. Forcada 2017). It now seems generally accepted that translation students should develop the capacity to decide on the deployment of language technologies by learning about the capabilities and limitations of the machines and tools with which they are and will be working (cf. Massey & Ehrensberger-Dow 2017). This type of knowledge has been referred to as MT literacy by Bowker and Buitrago Ciro (2019), whose list of component competences probably seems familiar to most institutions currently involved in translator training (e.g. comprehend the basics of MT systems; appreciate the wider implications associated with the use of MT; evaluate how MT-friendly texts are; create or modify a text so that it could be translated; modify MT output to improve its accuracy and readability). MT literacy can inform judgements about the appropriate genres, quality expectations, risks, and limitations that call for intervention by human translators. Although rarely mentioned in this context, those judgements also necessarily draw on the rich intercultural awareness that translators bring to their work. As intercultural mediators, they have been trained to recognize and deal with cultural differences, potential ambiguity, terminological inconsistencies as well as conceptual and lexical gaps as they transfer meaning from one language to another (e.g. Federici & Declercq 2019). This is very much in line with Earley and Ang’s (2003: 9) multidimensional concept of cultural intelligence, which they define as “a person’s capability for successful adaptation to new cultural settings”. The extension of this concept to “intercultural intelligence” captures the reality of translators shifting back and forth between the context of the source text and that of the target text. Conceptualising MT literacy as being at the interface between language-related artificial intelligence and intercultural intelligence allows for the integration of apparently opposite poles of the human-machine spectrum. Such a conceptualisation can provide the space to encourage the development of expertise in language technology and at the same time foster the uniquely human dimensions of intercultural mediation, intuition, creativity, and ethical judgement.
Further description: References Bowker, L. & J. Buitrago Ciro. 2019. Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Bingley: Emerald Publishing. Earley, P. C. & S. Ang. 2003. Cultural intelligence: Individual interactions across cultures. Stanford, CA: Stanford Business Books. Federici, F. M. & C. Declercq, eds. 2019. Intercultural Crisis Communication. London: Bloomsbury Press. Forcada, M. L. 2017. Making sense of neural machine translation. Translation Spaces 6 (2): 291–309. Hassan, H., A. Aue, C. Chen, V. Chowdhary, J. Clark, C. Federmann, X. Huang, M. Junczys-Dowmunt, W. Lewis, M. Li, S. Liu, T-Y. Liu, R. Luo, A., Menezes, T., Qin, F., Seide, X., Tan, F., Tian, L., Wu, S., Wu, Y., Xia, D., Zhang, Z., & Zhou, M. 2018. Achieving human parity on automatic Chinese to English news translation. arXiv:1803.05567. Martindale, M. J. & M. Carpuat. 2018. Fluency over accuracy: A pilot study in measuring user trust in imperfect MT. Proceedings of AMTA 2018 1: 13-25. Massey, G. & M. Ehrensberger-Dow. 2017. Machine learning – implications for translator education. Lebende Sprache 62 (2): 300-312.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Applied Linguistics
Organisational Unit: Institute of Translation and Interpreting (IUED)
Appears in collections:Publikationen Angewandte Linguistik

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