From Hyperbolic Geometry Back to Word Embeddings
Zhenisbek Assylbekov, Sultan Nurmukhamedov, Arsen Sheverdin, Thomas Mach
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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.repl4nlp-1.5.pdf
- Video:
- https://aclanthology.org/2022.repl4nlp-1.5.mp4
- Code
- soltustik/rhg
Export citation
@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
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.