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