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

Proceedings of NAACL-HLT 2019, pages 3597–3608 Minneapolis, Minnesota, June 2 - June 7, 2019. c⃝2019 Association for Computational Linguistics 3597 pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference Mandar Joshi† Eunsol Choi† Omer Levy‡ Daniel S. Weld† Luke Zettlemoyer†‡ † Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA {mandar90,eunsol,weld,lsz}@cs.washington.edu ‡ Facebook AI Research, Seattle {omerlevy,lsz}@fb.com Abstract Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This pa- per proposes new methods for learning and us- ing embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are computed as a compositional function on word representations, which is learned by maximiz- ing the pointwise mutual information (PMI) with the contexts in which the two words co- occur. We add these representations to the cross-sentence attention layer of existing in- ference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or replacing ex- isting word embeddings. Experiments show a gain of 2.7% on the recently released SQuAD 2.0 and 1.3% on MultiNLI. Our representa- tions also aid in better generalization with gains of around 6-7% on adversarial SQuAD datasets, and 8.8% on the adversarial entail- ment test set by Glockner et al. (2018). 1 Introduction Reasoning about relationships between pairs of words is crucial for cross sentence inference prob- lems such as question answering (QA) and natu- ral language inference (NLI). In NLI, for exam- ple, given the premise “golf is prohibitively expen- sive”, inferring that the hypothesis “golf is a cheap pastime” is a contradiction requires one to know that expensive and cheap are antonyms. Recent work (Glockner et al., 2018) has shown that cur- rent models, which rely heavily on unsupervised single-word embeddings, struggle to learn such re- lationships. In this paper, we show that they can be learned with word pair vectors (pair2vec1), 1https://github.com/mandarjoshi90/ pair2vec X Y Contexts with X and Y baths hot cold too X or too Y neither X nor Y in X, Y Portland Oregon the X metropolitan area in Y X International Airport in Y food X are maize, Y, etc crop wheat dry X, such as Y, more X circles appeared in Y fields X OS comes with Y play Android Google the X team at Y X is developed by Y Table 1: Example word pairs and their contexts. which are trained unsupervised, and which signif- icantly improve performance when added to exist- ing cross-sentence attention mechanisms. Unlike single-word representations, which typ- ically model the co-occurrence of a target word x with its context c, our word-pair representa- tions are learned by modeling the three-way co- occurrence between words (x, y) and the context c that ties them together, as seen in Table 1. While similar training signals have been used to learn models for ontology construction (Hearst, 1992; Snow et al., 2005; Turney, 2005; Shwartz et al., 2016) and knowledge base completion (Riedel et al., 2013), this paper …

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