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        <title>Identifying Context-Dependent Translations for Evaluation Set Production</title>
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    <abstract>A major impediment to the transition to contextual machine translation is the absence of good evaluation metrics and test sets. Sentences that require context to be translated correctly are rare in test sets, reducing the utility of standard corpus-level metrics such as COMET or BLEU. On the other hand, datasets that annotate such sentences are also rare, small in scale, and available for only a few languages. To address this, we modernize, generalize, and extend previous annotation pipelines to produce MultiPro, a tool that identifies subsets of parallel documents containing sentences that require context to correctly translate five phenomena: gender, formality, and animacy for pronouns, verb phrase ellipsis, and ambiguous noun inflections. The input to the pipeline is a set of hand-crafted, per-language, linguistically-informed rules that select contextual sentence pairs using coreference, part-of-speech, and morphological features provided by state-of-the-art tools. We apply this pipeline to seven languages pairs (EN into and out-of DE, ES, FR, IT, PL, PT, and RU) and two datasets (OpenSubtitles and WMT test sets), and validate its performance using both overlap with previous work and its ability to discriminate a contextual MT system from a sentence-based one. We release the MultiPro pipeline and data as open source.</abstract>
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%0 Conference Proceedings
%T Identifying Context-Dependent Translations for Evaluation Set Production
%A Wicks, Rachel
%A Post, Matt
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wicks-post-2023-identifying
%X A major impediment to the transition to contextual machine translation is the absence of good evaluation metrics and test sets. Sentences that require context to be translated correctly are rare in test sets, reducing the utility of standard corpus-level metrics such as COMET or BLEU. On the other hand, datasets that annotate such sentences are also rare, small in scale, and available for only a few languages. To address this, we modernize, generalize, and extend previous annotation pipelines to produce MultiPro, a tool that identifies subsets of parallel documents containing sentences that require context to correctly translate five phenomena: gender, formality, and animacy for pronouns, verb phrase ellipsis, and ambiguous noun inflections. The input to the pipeline is a set of hand-crafted, per-language, linguistically-informed rules that select contextual sentence pairs using coreference, part-of-speech, and morphological features provided by state-of-the-art tools. We apply this pipeline to seven languages pairs (EN into and out-of DE, ES, FR, IT, PL, PT, and RU) and two datasets (OpenSubtitles and WMT test sets), and validate its performance using both overlap with previous work and its ability to discriminate a contextual MT system from a sentence-based one. We release the MultiPro pipeline and data as open source.
%R 10.18653/v1/2023.wmt-1.42
%U https://aclanthology.org/2023.wmt-1.42
%U https://doi.org/10.18653/v1/2023.wmt-1.42
%P 452-467

Markdown (Informal)

[Identifying Context-Dependent Translations for Evaluation Set Production](https://aclanthology.org/2023.wmt-1.42) (Wicks & Post, WMT 2023)

• Identifying Context-Dependent Translations for Evaluation Set Production (Wicks & Post, WMT 2023)

ACL

• Rachel Wicks and Matt Post. 2023. Identifying Context-Dependent Translations for Evaluation Set Production. In Proceedings of the Eighth Conference on Machine Translation, pages 452–467, Singapore. Association for Computational Linguistics.