@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",
    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.",
}