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        <title>Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank</title>
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        <namePart type="given">Eleftheria</namePart>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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    <abstract>Detecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale. This work improves the prediction and annotation of fine-grained semantic divergences. We introduce a training strategy for multilingual BERT models by learning to rank synthetic divergent examples of varying granularity. We evaluate our models on the Rationalized English-French Semantic Divergences, a new dataset released with this work, consisting of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales. Learning to rank helps detect fine-grained sentence-level divergences more accurately than a strong sentence-level similarity model, while token-level predictions have the potential of further distinguishing between coarse and fine-grained divergences.</abstract>
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%0 Conference Proceedings
%T Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank
%A Briakou, Eleftheria
%A Carpuat, Marine
%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 briakou-carpuat-2020-detecting
%X Detecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale. This work improves the prediction and annotation of fine-grained semantic divergences. We introduce a training strategy for multilingual BERT models by learning to rank synthetic divergent examples of varying granularity. We evaluate our models on the Rationalized English-French Semantic Divergences, a new dataset released with this work, consisting of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales. Learning to rank helps detect fine-grained sentence-level divergences more accurately than a strong sentence-level similarity model, while token-level predictions have the potential of further distinguishing between coarse and fine-grained divergences.
%R 10.18653/v1/2020.emnlp-main.121
%U https://aclanthology.org/2020.emnlp-main.121
%U https://doi.org/10.18653/v1/2020.emnlp-main.121
%P 1563-1580

Markdown (Informal)

[Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank](https://aclanthology.org/2020.emnlp-main.121) (Briakou & Carpuat, EMNLP 2020)

• Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank (Briakou & Carpuat, EMNLP 2020)

ACL

• Eleftheria Briakou and Marine Carpuat. 2020. Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1563–1580, Online. Association for Computational Linguistics.