From Hyperbolic Geometry Back to Word Embeddings

Zhenisbek Assylbekov; Sultan Nurmukhamedov; Arsen Sheverdin; Thomas Mach

doi:10.18653/v1/2022.repl4nlp-1.5

From Hyperbolic Geometry Back to Word Embeddings

Zhenisbek Assylbekov, Sultan Nurmukhamedov, Arsen Sheverdin, Thomas Mach

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Abstract

We choose random points in the hyperbolic disc and claim that these points are already word representations. However, it is yet to be uncovered which point corresponds to which word of the human language of interest. This correspondence can be approximately established using a pointwise mutual information between words and recent alignment techniques.

Anthology ID:: 2022.repl4nlp-1.5
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:: 39–45
Language:
URL:: https://aclanthology.org/2022.repl4nlp-1.5/
DOI:: 10.18653/v1/2022.repl4nlp-1.5
Bibkey:
Cite (ACL):: Zhenisbek Assylbekov, Sultan Nurmukhamedov, Arsen Sheverdin, and Thomas Mach. 2022. From Hyperbolic Geometry Back to Word Embeddings. In Proceedings of the 7th Workshop on Representation Learning for NLP, pages 39–45, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):: From Hyperbolic Geometry Back to Word Embeddings (Assylbekov et al., RepL4NLP 2022)
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PDF:: https://aclanthology.org/2022.repl4nlp-1.5.pdf
Video:: https://aclanthology.org/2022.repl4nlp-1.5.mp4

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@inproceedings{assylbekov-etal-2022-hyperbolic,
    title = "From Hyperbolic Geometry Back to Word Embeddings",
    author = "Assylbekov, Zhenisbek  and
      Nurmukhamedov, Sultan  and
      Sheverdin, Arsen  and
      Mach, Thomas",
    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-1.5/",
    doi = "10.18653/v1/2022.repl4nlp-1.5",
    pages = "39--45",
    abstract = "We choose random points in the hyperbolic disc and claim that these points are already word representations. However, it is yet to be uncovered which point corresponds to which word of the human language of interest. This correspondence can be approximately established using a pointwise mutual information between words and recent alignment techniques."
}

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%0 Conference Proceedings
%T From Hyperbolic Geometry Back to Word Embeddings
%A Assylbekov, Zhenisbek
%A Nurmukhamedov, Sultan
%A Sheverdin, Arsen
%A Mach, Thomas
%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 assylbekov-etal-2022-hyperbolic
%X We choose random points in the hyperbolic disc and claim that these points are already word representations. However, it is yet to be uncovered which point corresponds to which word of the human language of interest. This correspondence can be approximately established using a pointwise mutual information between words and recent alignment techniques.
%R 10.18653/v1/2022.repl4nlp-1.5
%U https://aclanthology.org/2022.repl4nlp-1.5/
%U https://doi.org/10.18653/v1/2022.repl4nlp-1.5
%P 39-45

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Markdown (Informal)

[From Hyperbolic Geometry Back to Word Embeddings](https://aclanthology.org/2022.repl4nlp-1.5/) (Assylbekov et al., RepL4NLP 2022)

From Hyperbolic Geometry Back to Word Embeddings (Assylbekov et al., RepL4NLP 2022)

ACL

Zhenisbek Assylbekov, Sultan Nurmukhamedov, Arsen Sheverdin, and Thomas Mach. 2022. From Hyperbolic Geometry Back to Word Embeddings. In Proceedings of the 7th Workshop on Representation Learning for NLP, pages 39–45, Dublin, Ireland. Association for Computational Linguistics.