@inproceedings{volpi-malago-2020-evaluating,
title = "Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks",
author = "Volpi, Riccardo and
Malag{\`o}, Luigi",
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.9",
doi = "10.18653/v1/2020.repl4nlp-1.9",
pages = "61--71",
abstract = "Skip-Gram is a simple, but effective, model to learn a word embedding mapping by estimating a conditional probability distribution for each word of the dictionary. In the context of Information Geometry, these distributions form a Riemannian statistical manifold, where word embeddings are interpreted as vectors in the tangent bundle of the manifold. In this paper we show how the choice of the geometry on the manifold allows impacts on the performances both on intrinsic and extrinsic tasks, in function of a deformation parameter alpha.",
}
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<abstract>Skip-Gram is a simple, but effective, model to learn a word embedding mapping by estimating a conditional probability distribution for each word of the dictionary. In the context of Information Geometry, these distributions form a Riemannian statistical manifold, where word embeddings are interpreted as vectors in the tangent bundle of the manifold. In this paper we show how the choice of the geometry on the manifold allows impacts on the performances both on intrinsic and extrinsic tasks, in function of a deformation parameter alpha.</abstract>
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%0 Conference Proceedings
%T Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks
%A Volpi, Riccardo
%A Malagò, Luigi
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F volpi-malago-2020-evaluating
%X Skip-Gram is a simple, but effective, model to learn a word embedding mapping by estimating a conditional probability distribution for each word of the dictionary. In the context of Information Geometry, these distributions form a Riemannian statistical manifold, where word embeddings are interpreted as vectors in the tangent bundle of the manifold. In this paper we show how the choice of the geometry on the manifold allows impacts on the performances both on intrinsic and extrinsic tasks, in function of a deformation parameter alpha.
%R 10.18653/v1/2020.repl4nlp-1.9
%U https://aclanthology.org/2020.repl4nlp-1.9
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.9
%P 61-71
Markdown (Informal)
[Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks](https://aclanthology.org/2020.repl4nlp-1.9) (Volpi & Malagò, RepL4NLP 2020)
ACL