  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 on word representations, which is learned by maximizing 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
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 SQuAD2.0 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).
