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        <title>Translation Artifacts in Cross-lingual Transfer Learning</title>
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    <name type="personal">
        <namePart type="given">Mikel</namePart>
        <namePart type="family">Artetxe</namePart>
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        <namePart type="family">Labaka</namePart>
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    <name type="personal">
        <namePart type="given">Eneko</namePart>
        <namePart type="family">Agirre</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Both human and machine translation play a central role in cross-lingual transfer learning: many multilingual datasets have been created through professional translation services, and using machine translation to translate either the test set or the training set is a widely used transfer technique. In this paper, we show that such translation process can introduce subtle artifacts that have a notable impact in existing cross-lingual models. For instance, in natural language inference, translating the premise and the hypothesis independently can reduce the lexical overlap between them, which current models are highly sensitive to. We show that some previous findings in cross-lingual transfer learning need to be reconsidered in the light of this phenomenon. Based on the gained insights, we also improve the state-of-the-art in XNLI for the translate-test and zero-shot approaches by 4.3 and 2.8 points, respectively.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.618</identifier>
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        <date>2020-11</date>
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            <start>7674</start>
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%0 Conference Proceedings
%T Translation Artifacts in Cross-lingual Transfer Learning
%A Artetxe, Mikel
%A Labaka, Gorka
%A Agirre, Eneko
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F artetxe-etal-2020-translation
%X Both human and machine translation play a central role in cross-lingual transfer learning: many multilingual datasets have been created through professional translation services, and using machine translation to translate either the test set or the training set is a widely used transfer technique. In this paper, we show that such translation process can introduce subtle artifacts that have a notable impact in existing cross-lingual models. For instance, in natural language inference, translating the premise and the hypothesis independently can reduce the lexical overlap between them, which current models are highly sensitive to. We show that some previous findings in cross-lingual transfer learning need to be reconsidered in the light of this phenomenon. Based on the gained insights, we also improve the state-of-the-art in XNLI for the translate-test and zero-shot approaches by 4.3 and 2.8 points, respectively.
%R 10.18653/v1/2020.emnlp-main.618
%U https://aclanthology.org/2020.emnlp-main.618/
%U https://doi.org/10.18653/v1/2020.emnlp-main.618
%P 7674-7684

Markdown (Informal)

[Translation Artifacts in Cross-lingual Transfer Learning](https://aclanthology.org/2020.emnlp-main.618/) (Artetxe et al., EMNLP 2020)

• Translation Artifacts in Cross-lingual Transfer Learning (Artetxe et al., EMNLP 2020)

ACL

• Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2020. Translation Artifacts in Cross-lingual Transfer Learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7674–7684, Online. Association for Computational Linguistics.