@inproceedings{jawanpuria-etal-2020-learning,
title = "Learning Geometric Word Meta-Embeddings",
author = "Jawanpuria, Pratik and
N T V, Satya Dev and
Kunchukuttan, Anoop and
Mishra, Bamdev",
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-1.6",
doi = "10.18653/v1/2020.repl4nlp-1.6",
pages = "39--44",
abstract = "We propose a geometric framework for learning meta-embeddings of words from different embedding sources. Our framework transforms the embeddings into a common latent space, where, for example, simple averaging or concatenation of different embeddings (of a given word) is more amenable. The proposed latent space arises from two particular geometric transformations - source embedding specific orthogonal rotations and a common Mahalanobis metric scaling. Empirical results on several word similarity and word analogy benchmarks illustrate the efficacy of the proposed framework.",
}
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%0 Conference Proceedings
%T Learning Geometric Word Meta-Embeddings
%A Jawanpuria, Pratik
%A N T V, Satya Dev
%A Kunchukuttan, Anoop
%A Mishra, Bamdev
%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 jawanpuria-etal-2020-learning
%X We propose a geometric framework for learning meta-embeddings of words from different embedding sources. Our framework transforms the embeddings into a common latent space, where, for example, simple averaging or concatenation of different embeddings (of a given word) is more amenable. The proposed latent space arises from two particular geometric transformations - source embedding specific orthogonal rotations and a common Mahalanobis metric scaling. Empirical results on several word similarity and word analogy benchmarks illustrate the efficacy of the proposed framework.
%R 10.18653/v1/2020.repl4nlp-1.6
%U https://aclanthology.org/2020.repl4nlp-1.6
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.6
%P 39-44
Markdown (Informal)
[Learning Geometric Word Meta-Embeddings](https://aclanthology.org/2020.repl4nlp-1.6) (Jawanpuria et al., RepL4NLP 2020)
ACL
- Pratik Jawanpuria, Satya Dev N T V, Anoop Kunchukuttan, and Bamdev Mishra. 2020. Learning Geometric Word Meta-Embeddings. In Proceedings of the 5th Workshop on Representation Learning for NLP, pages 39–44, Online. Association for Computational Linguistics.