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        <title>Evaluation and Large-scale Training for Contextual Machine Translation</title>
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            <title>Proceedings of the Ninth Conference on Machine Translation</title>
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    <abstract>Despite the fact that context is known to be vital for resolving a range of translation ambiguities, most traditional machine translation systems continue to be trained and to operate at the sentence level. A common explanation is the lack of document-level annotations for existing training data. This work investigates whether having such annotations would be helpful for training traditional MT systems at scale. We build large-scale, state-of-the-art contextual MT systems into German, French, and Russian, fixing the datasets while comparing the effect of sourcing contextual training samples from both parallel and back-translated data. We then evaluate these contextual models across a range of contextual test sets from the literature, where we find that (a) document annotations from both mined parallel and back-translated monolingual data are helpful, but that the best contextual MT systems do not draw contextual samples from the parallel data. We also make two points related to evaluation: (b) contrastive score-based metrics on challenge sets are not discriminative; instead, models must be tested directly on their ability to generate correct outputs, and (c) standard corpus-level metrics such as COMET work best in settings that are dense in contextual phenomena.</abstract>
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%0 Conference Proceedings
%T Evaluation and Large-scale Training for Contextual Machine Translation
%A Post, Matt
%A Junczys-Dowmunt, Marcin
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F post-junczys-dowmunt-2024-evaluation
%X Despite the fact that context is known to be vital for resolving a range of translation ambiguities, most traditional machine translation systems continue to be trained and to operate at the sentence level. A common explanation is the lack of document-level annotations for existing training data. This work investigates whether having such annotations would be helpful for training traditional MT systems at scale. We build large-scale, state-of-the-art contextual MT systems into German, French, and Russian, fixing the datasets while comparing the effect of sourcing contextual training samples from both parallel and back-translated data. We then evaluate these contextual models across a range of contextual test sets from the literature, where we find that (a) document annotations from both mined parallel and back-translated monolingual data are helpful, but that the best contextual MT systems do not draw contextual samples from the parallel data. We also make two points related to evaluation: (b) contrastive score-based metrics on challenge sets are not discriminative; instead, models must be tested directly on their ability to generate correct outputs, and (c) standard corpus-level metrics such as COMET work best in settings that are dense in contextual phenomena.
%U https://aclanthology.org/2024.wmt-1.112
%P 1125-1139

Markdown (Informal)

[Evaluation and Large-scale Training for Contextual Machine Translation](https://aclanthology.org/2024.wmt-1.112) (Post & Junczys-Dowmunt, WMT 2024)

• Evaluation and Large-scale Training for Contextual Machine Translation (Post & Junczys-Dowmunt, WMT 2024)

ACL

• Matt Post and Marcin Junczys-Dowmunt. 2024. Evaluation and Large-scale Training for Contextual Machine Translation. In Proceedings of the Ninth Conference on Machine Translation, pages 1125–1139, Miami, Florida, USA. Association for Computational Linguistics.