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        <title>Evaluating the Impact of Verbal Multiword Expressions on Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Linfeng</namePart>
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    <name type="personal">
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            <title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Viviane</namePart>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component words. In this study, we analyze the impact of three VMWE categories—verbal idioms, verb-particle constructions, and light verb constructions—on machine translation quality from English to multiple languages. Using both established multiword expression datasets and standard machine translation datasets, we evaluate how state-of-the-art translation systems handle these expressions. Our experimental results consistently show that VMWEs negatively affect translation quality, with deeper analysis indicating that this degradation is primarily attributable to the VMWE itself rather than general sentence-level difficulty. We release our code and evaluation framework to test new MT systems for the community.</abstract>
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        <url>https://aclanthology.org/2026.acl-long.698/</url>
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%0 Conference Proceedings
%T Evaluating the Impact of Verbal Multiword Expressions on Machine Translation
%A Liu, Linfeng
%A Ghosh, Saptarshi
%A Jiang, Tianyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-evaluating
%X Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component words. In this study, we analyze the impact of three VMWE categories—verbal idioms, verb-particle constructions, and light verb constructions—on machine translation quality from English to multiple languages. Using both established multiword expression datasets and standard machine translation datasets, we evaluate how state-of-the-art translation systems handle these expressions. Our experimental results consistently show that VMWEs negatively affect translation quality, with deeper analysis indicating that this degradation is primarily attributable to the VMWE itself rather than general sentence-level difficulty. We release our code and evaluation framework to test new MT systems for the community.
%U https://aclanthology.org/2026.acl-long.698/
%P 15291-15319

Markdown (Informal)

[Evaluating the Impact of Verbal Multiword Expressions on Machine Translation](https://aclanthology.org/2026.acl-long.698/) (Liu et al., ACL 2026)

• Evaluating the Impact of Verbal Multiword Expressions on Machine Translation (Liu et al., ACL 2026)

ACL

• Linfeng Liu, Saptarshi Ghosh, and Tianyu Jiang. 2026. Evaluating the Impact of Verbal Multiword Expressions on Machine Translation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15291–15319, San Diego, California, United States. Association for Computational Linguistics.