Satya Dev N T V
Learning Geometric Word Meta-Embeddings
Pratik Jawanpuria | Satya Dev N T V | Anoop Kunchukuttan | Bamdev Mishra
Proceedings of the 5th Workshop on Representation Learning for NLP
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.