Learning Geometric Word Meta-Embeddings
Pratik Jawanpuria, N. T. V. Satya Dev, Anoop Kunchukuttan, Bamdev Mishra · 2020
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.