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        <title>Improving word mover’s distance by leveraging self-attention matrix</title>
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    <name type="personal">
        <namePart type="given">Hiroaki</namePart>
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        <namePart type="given">Sho</namePart>
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    <name type="personal">
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        <namePart type="family">Shimodaira</namePart>
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            <namePart type="given">Juan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Measuring the semantic similarity between two sentences is still an important task. The word mover’s distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does not utilize word order, making it challenging to distinguish sentences with significant overlaps of similar words, even if they are semantically very different. Here, we attempt to improve WMD by incorporating the sentence structure represented by BERT’s self-attention matrix (SAM). The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences. Experiments demonstrate the proposed method enhances WMD and its variants in paraphrase identification with near-equivalent performance in semantic textual similarity.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.746</identifier>
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        <date>2023-12</date>
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%0 Conference Proceedings
%T Improving word mover’s distance by leveraging self-attention matrix
%A Yamagiwa, Hiroaki
%A Yokoi, Sho
%A Shimodaira, Hidetoshi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yamagiwa-etal-2023-improving
%X Measuring the semantic similarity between two sentences is still an important task. The word mover’s distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does not utilize word order, making it challenging to distinguish sentences with significant overlaps of similar words, even if they are semantically very different. Here, we attempt to improve WMD by incorporating the sentence structure represented by BERT’s self-attention matrix (SAM). The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences. Experiments demonstrate the proposed method enhances WMD and its variants in paraphrase identification with near-equivalent performance in semantic textual similarity.
%R 10.18653/v1/2023.findings-emnlp.746
%U https://aclanthology.org/2023.findings-emnlp.746
%U https://doi.org/10.18653/v1/2023.findings-emnlp.746
%P 11160-11183

Markdown (Informal)

[Improving word mover’s distance by leveraging self-attention matrix](https://aclanthology.org/2023.findings-emnlp.746) (Yamagiwa et al., Findings 2023)

• Improving word mover’s distance by leveraging self-attention matrix (Yamagiwa et al., Findings 2023)

ACL

• Hiroaki Yamagiwa, Sho Yokoi, and Hidetoshi Shimodaira. 2023. Improving word mover’s distance by leveraging self-attention matrix. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11160–11183, Singapore. Association for Computational Linguistics.