Arsen Sheverdin


2022

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From Hyperbolic Geometry Back to Word Embeddings
Zhenisbek Assylbekov | Sultan Nurmukhamedov | Arsen Sheverdin | Thomas Mach
Proceedings of the 7th Workshop on Representation Learning for NLP

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.