Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence
  Inference   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).

13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas