Learning Probabilistic Sentence Representations from Paraphrases

Mingda Chen, Kevin Gimpel


Abstract
Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation for sentences. In this paper we define probabilistic models that produce distributions for sentences. Our best-performing model treats each word as a linear transformation operator applied to a multivariate Gaussian distribution. We train our models on paraphrases and demonstrate that they naturally capture sentence specificity. While our proposed model achieves the best performance overall, we also show that specificity is represented by simpler architectures via the norm of the sentence vectors. Qualitative analysis shows that our probabilistic model captures sentential entailment and provides ways to analyze the specificity and preciseness of individual words.
Anthology ID:
2020.repl4nlp-1.3
Volume:
Proceedings of the 5th Workshop on Representation Learning for NLP
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | RepL4NLP | WS
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
17–23
Language:
URL:
https://www.aclweb.org/anthology/2020.repl4nlp-1.3
DOI:
10.18653/v1/2020.repl4nlp-1.3
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.repl4nlp-1.3.pdf
Video:
 http://slideslive.com/38929769