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        <namePart type="given">Vedanuj</namePart>
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        <namePart type="given">Shruti</namePart>
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    <name type="personal">
        <namePart type="given">Angela</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Machine Translation (MT) has been widely used for cross-lingual classification, either by translating the test set into English and running inference with a monolingual model (translate-test), or translating the training set into the target languages and finetuning a multilingual model (translate-train). However, most research in the area focuses on the multilingual models rather than the MT component. We show that, by using a stronger MT system and mitigating the mismatch between training on original text and running inference on machine translated text, translate-test can do substantially better than previously assumed. The optimal approach, however, is highly task dependent, as we identify various sources of cross-lingual transfer gap that affect different tasks and approaches differently. Our work calls into question the dominance of multilingual models for cross-lingual classification, and prompts to pay more attention to MT-based baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.399</identifier>
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        <date>2023-12</date>
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            <start>6489</start>
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%0 Conference Proceedings
%T Revisiting Machine Translation for Cross-lingual Classification
%A Artetxe, Mikel
%A Goswami, Vedanuj
%A Bhosale, Shruti
%A Fan, Angela
%A Zettlemoyer, Luke
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F artetxe-etal-2023-revisiting
%X Machine Translation (MT) has been widely used for cross-lingual classification, either by translating the test set into English and running inference with a monolingual model (translate-test), or translating the training set into the target languages and finetuning a multilingual model (translate-train). However, most research in the area focuses on the multilingual models rather than the MT component. We show that, by using a stronger MT system and mitigating the mismatch between training on original text and running inference on machine translated text, translate-test can do substantially better than previously assumed. The optimal approach, however, is highly task dependent, as we identify various sources of cross-lingual transfer gap that affect different tasks and approaches differently. Our work calls into question the dominance of multilingual models for cross-lingual classification, and prompts to pay more attention to MT-based baselines.
%R 10.18653/v1/2023.emnlp-main.399
%U https://aclanthology.org/2023.emnlp-main.399
%U https://doi.org/10.18653/v1/2023.emnlp-main.399
%P 6489-6499

Markdown (Informal)

[Revisiting Machine Translation for Cross-lingual Classification](https://aclanthology.org/2023.emnlp-main.399) (Artetxe et al., EMNLP 2023)

• Revisiting Machine Translation for Cross-lingual Classification (Artetxe et al., EMNLP 2023)

ACL

• Mikel Artetxe, Vedanuj Goswami, Shruti Bhosale, Angela Fan, and Luke Zettlemoyer. 2023. Revisiting Machine Translation for Cross-lingual Classification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6489–6499, Singapore. Association for Computational Linguistics.