%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