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        <title>Language-Informed Beam Search Decoding for Multilingual Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Yilin</namePart>
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        <dateIssued>2024-08</dateIssued>
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            <title>Findings of the Association for Computational Linguistics ACL 2024</title>
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            <namePart type="given">Lun-Wei</namePart>
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            <namePart type="given">Andre</namePart>
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            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
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    <abstract>Beam search decoding is the de-facto method for decoding auto-regressive Neural Machine Translation (NMT) models, including multilingual NMT where the target language is specified as an input. However, decoding multilingual NMT models commonly produces off-target translations – yielding translation outputs not in the intended language.In this paper, we first conduct an error analysis of off-target translations for a strong multilingual NMT model and identify how these decodings are produced during beam search. We then propose Language-informed Beam Search (LiBS), a general decoding algorithm incorporating an off-the-shelf Language Identification (LiD) model into beam search decoding to reduce off-target translations. LiBS is an inference-time procedure that is NMT-model agnostic and does not require any additional parallel data. Results show that our proposed LiBS algorithm on average improves +1.1 BLEU and +0.9 BLEU on WMT and OPUS datasets, and reduces off-target rates from 22.9% to 7.7% and 65.8% to 25.3% respectively.</abstract>
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        <url>https://aclanthology.org/2024.findings-acl.932</url>
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        <date>2024-08</date>
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            <start>15761</start>
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%0 Conference Proceedings
%T Language-Informed Beam Search Decoding for Multilingual Machine Translation
%A Yang, Yilin
%A Lee, Stefan
%A Tadepalli, Prasad
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F yang-etal-2024-language-informed
%X Beam search decoding is the de-facto method for decoding auto-regressive Neural Machine Translation (NMT) models, including multilingual NMT where the target language is specified as an input. However, decoding multilingual NMT models commonly produces off-target translations – yielding translation outputs not in the intended language.In this paper, we first conduct an error analysis of off-target translations for a strong multilingual NMT model and identify how these decodings are produced during beam search. We then propose Language-informed Beam Search (LiBS), a general decoding algorithm incorporating an off-the-shelf Language Identification (LiD) model into beam search decoding to reduce off-target translations. LiBS is an inference-time procedure that is NMT-model agnostic and does not require any additional parallel data. Results show that our proposed LiBS algorithm on average improves +1.1 BLEU and +0.9 BLEU on WMT and OPUS datasets, and reduces off-target rates from 22.9% to 7.7% and 65.8% to 25.3% respectively.
%U https://aclanthology.org/2024.findings-acl.932
%P 15761-15772

Markdown (Informal)

[Language-Informed Beam Search Decoding for Multilingual Machine Translation](https://aclanthology.org/2024.findings-acl.932) (Yang et al., Findings 2024)

• Language-Informed Beam Search Decoding for Multilingual Machine Translation (Yang et al., Findings 2024)

ACL

• Yilin Yang, Stefan Lee, and Prasad Tadepalli. 2024. Language-Informed Beam Search Decoding for Multilingual Machine Translation. In Findings of the Association for Computational Linguistics ACL 2024, pages 15761–15772, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.