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Do online machine translation systems care for context? What about a GPT model?

Castilho, Sheila orcid logoORCID: 0000-0002-8416-6555, Mallon, Clodagh, Meister, Rahel and Yue, Shengya (2023) Do online machine translation systems care for context? What about a GPT model? In: 24th Annual Conference of the European Association for Machine Translation (EAMT 2023), 12-15 June 2023, Tampere, Finland.

Abstract
This paper addresses the challenges of evaluating document-level machine translation (MT) in the context of recent advances in context-aware neural machine translation (NMT). It investigates how well online MT systems deal with six contextrelated issues, namely lexical ambiguity, grammatical gender, grammatical number, reference, ellipsis, and terminology, when a larger context span containing the solution for those issues is given as input. Results are compared to the translation outputs from the online ChatGPT. Our results show that, while the change of punctuation in the input yields great variability in the output translations, the context position does not seem to have a great impact. Moreover, the GPT model seems to outperform the NMT systems but performs poorly for Irish.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine translating
Humanities > German language
Humanities > Irish language
Humanities > Language
Humanities > Translating and interpreting
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies
Research Initiatives and Centres > Centre for Translation and Textual Studies (CTTS)
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 24th Annual Conference of the European Association for Machine Translation (EAMT 2023). . European Association for Machine Translation (EAMT).
Publisher:European Association for Machine Translation (EAMT)
Official URL:https://aclanthology.org/2023.eamt-1.39
Copyright Information:© 2023 The Authors.
Funders:Science Foundation Ireland at ADAPT [13/RC/2106P2].
ID Code:28297
Deposited On:28 Apr 2023 12:23 by Sheila Castilho . Last Modified 16 Nov 2023 16:10
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