Binary Encoded Word Mover’s Distance

Christian Johnson

doi:10.18653/v1/2022.repl4nlp-1.17

Binary Encoded Word Mover’s Distance

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Abstract

Word Mover’s Distance is a textual distance metric which calculates the minimum transport cost between two sets of word embeddings. This metric achieves impressive results on semantic similarity tasks, but is slow and difficult to scale due to the large number of floating point calculations. This paper demonstrates that by combining pre-existing lower bounds with binary encoded word vectors, the metric can be rendered highly efficient in terms of computation time and memory while still maintaining accuracy on several textual similarity tasks.

Anthology ID:: 2022.repl4nlp-1.17
Volume:: Proceedings of the 7th Workshop on Representation Learning for NLP
Month:: May
Year:: 2022
Address:: Dublin, Ireland
Editors:: Spandana Gella, He He, Bodhisattwa Prasad Majumder, Burcu Can, Eleonora Giunchiglia, Samuel Cahyawijaya, Sewon Min, Maximilian Mozes, Xiang Lorraine Li, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Laura Rimell, Chris Dyer
Venue:: RepL4NLP
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 167–172
Language:
URL:: https://aclanthology.org/2022.repl4nlp-1.17/
DOI:: 10.18653/v1/2022.repl4nlp-1.17
Bibkey:
Cite (ACL):: Christian Johnson. 2022. Binary Encoded Word Mover’s Distance. In Proceedings of the 7th Workshop on Representation Learning for NLP, pages 167–172, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):: Binary Encoded Word Mover’s Distance (Johnson, RepL4NLP 2022)
Copy Citation:
PDF:: https://aclanthology.org/2022.repl4nlp-1.17.pdf
Video:: https://aclanthology.org/2022.repl4nlp-1.17.mp4

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@inproceedings{johnson-2022-binary,
    title = "Binary Encoded Word Mover{'}s Distance",
    author = "Johnson, Christian",
    editor = "Gella, Spandana  and
      He, He  and
      Majumder, Bodhisattwa Prasad  and
      Can, Burcu  and
      Giunchiglia, Eleonora  and
      Cahyawijaya, Samuel  and
      Min, Sewon  and
      Mozes, Maximilian  and
      Li, Xiang Lorraine  and
      Augenstein, Isabelle  and
      Rogers, Anna  and
      Cho, Kyunghyun  and
      Grefenstette, Edward  and
      Rimell, Laura  and
      Dyer, Chris",
    booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.repl4nlp-1.17/",
    doi = "10.18653/v1/2022.repl4nlp-1.17",
    pages = "167--172",
    abstract = "Word Mover{'}s Distance is a textual distance metric which calculates the minimum transport cost between two sets of word embeddings. This metric achieves impressive results on semantic similarity tasks, but is slow and difficult to scale due to the large number of floating point calculations. This paper demonstrates that by combining pre-existing lower bounds with binary encoded word vectors, the metric can be rendered highly efficient in terms of computation time and memory while still maintaining accuracy on several textual similarity tasks."
}

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%0 Conference Proceedings
%T Binary Encoded Word Mover’s Distance
%A Johnson, Christian
%Y Gella, Spandana
%Y He, He
%Y Majumder, Bodhisattwa Prasad
%Y Can, Burcu
%Y Giunchiglia, Eleonora
%Y Cahyawijaya, Samuel
%Y Min, Sewon
%Y Mozes, Maximilian
%Y Li, Xiang Lorraine
%Y Augenstein, Isabelle
%Y Rogers, Anna
%Y Cho, Kyunghyun
%Y Grefenstette, Edward
%Y Rimell, Laura
%Y Dyer, Chris
%S Proceedings of the 7th Workshop on Representation Learning for NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F johnson-2022-binary
%X Word Mover’s Distance is a textual distance metric which calculates the minimum transport cost between two sets of word embeddings. This metric achieves impressive results on semantic similarity tasks, but is slow and difficult to scale due to the large number of floating point calculations. This paper demonstrates that by combining pre-existing lower bounds with binary encoded word vectors, the metric can be rendered highly efficient in terms of computation time and memory while still maintaining accuracy on several textual similarity tasks.
%R 10.18653/v1/2022.repl4nlp-1.17
%U https://aclanthology.org/2022.repl4nlp-1.17/
%U https://doi.org/10.18653/v1/2022.repl4nlp-1.17
%P 167-172

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Markdown (Informal)

[Binary Encoded Word Mover’s Distance](https://aclanthology.org/2022.repl4nlp-1.17/) (Johnson, RepL4NLP 2022)

Binary Encoded Word Mover’s Distance (Johnson, RepL4NLP 2022)

ACL

Christian Johnson. 2022. Binary Encoded Word Mover’s Distance. In Proceedings of the 7th Workshop on Representation Learning for NLP, pages 167–172, Dublin, Ireland. Association for Computational Linguistics.