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Investigating contextual Influence in document-level translation

Nayak, Prashanth orcid logoORCID: 0000-0003-1962-9135, Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099, Kelleher, John D. orcid logoORCID: 0000-0001-6462-3248 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2022) Investigating contextual Influence in document-level translation. Information, 13 (5). ISSN 2078-2489

Abstract
Current state-of-the-art neural machine translation (NMT) architectures usually do not take document-level context into account. However, the document-level context of a source sentence to be translated could encode valuable information to guide the MT model to generate a better translation. In recent times, MT researchers have turned their focus to this line of MT research. As an example, hierarchical attention network (HAN) models use document-level context for translation prediction. In this work, we studied translations produced by the HAN-based MT systems. We examined how contextual information improves translation in document-level NMT. More specifically, we investigated why context-aware models such as HAN perform better than vanilla baseline NMT systems that do not take context into account. We considered Hindi-to-English, Spanish-to-English and Chinese-to-English for our investigation. We experimented with the formation of conditional context (i.e., neighbouring sentences) of the source sentences to be translated in HAN to predict their target translations. Interestingly, we observed that the quality of the target translations of specific source sentences highly relates to the context in which the source sentences appear. Based on their sensitivity to context, we classify our test set sentences into three categories, i.e., context-sensitive, context-insensitive and normal. We believe that this categorization may change the way in which context is utilized in document-level translation.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:machine translation; neural machine translation; context‑aware translation; document translation
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:MDPI
Official URL:https://dx.doi.org/
Copyright Information:© 2022 The Authors.
ID Code:27453
Deposited On:28 Jul 2022 17:33 by Thomas Murtagh . Last Modified 11 May 2023 13:31
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