Focused Concatenation for Context-Aware Neural Machine Translation

Lorenzo Lupo, Marco Dinarelli, Laurent Besacier


Abstract
A straightforward approach to context-aware neural machine translation consists in feeding the standard encoder-decoder architecture with a window of consecutive sentences, formed by the current sentence and a number of sentences from its context concatenated to it. In this work, we propose an improved concatenation approach that encourages the model to focus on the translation of the current sentence, discounting the loss generated by target context. We also propose an additional improvement that strengthen the notion of sentence boundaries and of relative sentence distance, facilitating model compliance to the context-discounted objective. We evaluate our approach with both average-translation quality metrics and contrastive test sets for the translation of inter-sentential discourse phenomena, proving its superiority to the vanilla concatenation approach and other sophisticated context-aware systems.
Anthology ID:
2022.wmt-1.77
Volume:
Proceedings of the Seventh Conference on Machine Translation (WMT)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Venue:
WMT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
830–842
Language:
URL:
https://aclanthology.org/2022.wmt-1.77
DOI:
Bibkey:
Cite (ACL):
Lorenzo Lupo, Marco Dinarelli, and Laurent Besacier. 2022. Focused Concatenation for Context-Aware Neural Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 830–842, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Focused Concatenation for Context-Aware Neural Machine Translation (Lupo et al., WMT 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.wmt-1.77.pdf