Learning Geometric Word Meta-Embeddings
Pratik Jawanpuria, Satya Dev N T V, Anoop Kunchukuttan, Bamdev Mishra
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.- Anthology ID:
- 2020.repl4nlp-1.6
- Volume:
- Proceedings of the 5th Workshop on Representation Learning for NLP
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Spandana Gella, Johannes Welbl, Marek Rei, Fabio Petroni, Patrick Lewis, Emma Strubell, Minjoon Seo, Hannaneh Hajishirzi
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 39–44
- Language:
- URL:
- https://aclanthology.org/2020.repl4nlp-1.6
- DOI:
- 10.18653/v1/2020.repl4nlp-1.6
- Cite (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.
- Cite (Informal):
- Learning Geometric Word Meta-Embeddings (Jawanpuria et al., RepL4NLP 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.repl4nlp-1.6.pdf