Which Evaluations Uncover Sense Representations that Actually Make Sense?

Jordan Boyd-Graber, Fenfei Guo, Leah Findlater, Mohit Iyyer


Abstract
Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word’s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language’s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings.
Anthology ID:
2020.lrec-1.214
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1727–1738
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.214
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.214.pdf