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        <title>Predicting Human Translation Difficulty Using Automatic Word Alignment</title>
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    <abstract>Translation difficulty arises when translators are required to resolve translation ambiguity from multiple possible translations. Translation difficulty can be measured by recording the diversity of responses provided by human translators and the time taken to provide these responses, but these behavioral measures are costly and do not scale. In this work, we use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty. We evaluate our approach using behavioural data from translations provided both in and out of context, and report results that improve on a previous embedding-based approach (Thompson et al., 2020). Our work can therefore contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty.</abstract>
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%0 Conference Proceedings
%T Predicting Human Translation Difficulty Using Automatic Word Alignment
%A Lim, Zheng Wei
%A Cohn, Trevor
%A Kemp, Charles
%A Vylomova, Ekaterina
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lim-etal-2023-predicting
%X Translation difficulty arises when translators are required to resolve translation ambiguity from multiple possible translations. Translation difficulty can be measured by recording the diversity of responses provided by human translators and the time taken to provide these responses, but these behavioral measures are costly and do not scale. In this work, we use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty. We evaluate our approach using behavioural data from translations provided both in and out of context, and report results that improve on a previous embedding-based approach (Thompson et al., 2020). Our work can therefore contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty.
%R 10.18653/v1/2023.findings-acl.736
%U https://aclanthology.org/2023.findings-acl.736
%U https://doi.org/10.18653/v1/2023.findings-acl.736
%P 11590-11601

Markdown (Informal)

[Predicting Human Translation Difficulty Using Automatic Word Alignment](https://aclanthology.org/2023.findings-acl.736) (Lim et al., Findings 2023)

• Predicting Human Translation Difficulty Using Automatic Word Alignment (Lim et al., Findings 2023)

ACL

• Zheng Wei Lim, Trevor Cohn, Charles Kemp, and Ekaterina Vylomova. 2023. Predicting Human Translation Difficulty Using Automatic Word Alignment. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11590–11601, Toronto, Canada. Association for Computational Linguistics.