pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference

Mandar Joshi, Eunsol Choi, Omer Levy, Daniel Weld, Luke Zettlemoyer

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Abstract
Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This paper proposes new methods for learning and using embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are computed as a compositional function of each word’s representation, which is learned by maximizing the pointwise mutual information (PMI) with the contexts in which the the two words co-occur. We add these representations to the cross-sentence attention layer of existing inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or replacing existing word embeddings. Experiments show a gain of 2.7% on the recently released SQuAD 2.0 and 1.3% on MultiNLI. Our representations also aid in better generalization with gains of around 6-7% on adversarial SQuAD datasets, and 8.8% on the adversarial entailment test set by Glockner et al. (2018).
Anthology ID:
N19-1362
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3597–3608
Language:
URL:
https://aclanthology.org/N19-1362
DOI:
10.18653/v1/N19-1362
Bibkey:
Cite (ACL):
Mandar Joshi, Eunsol Choi, Omer Levy, Daniel Weld, and Luke Zettlemoyer. 2019. pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3597–3608, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference (Joshi et al., NAACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/N19-1362.pdf
Code
 mandarjoshi90/pair2vec +  additional community code
Data
MultiNLISNLISQuAD