Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Effective Character-augmented Word Embedding for Machine Reading
  Comprehension   Machine reading comprehension is a task to model relationship between passage
and query. In terms of deep learning framework, most of state-of-the-art models
simply concatenate word and character level representations, which has been
shown suboptimal for the concerned task. In this paper, we empirically explore
different integration strategies of word and character embeddings and propose a
character-augmented reader which attends character-level representation to
augment word embedding with a short list to improve word representations,
especially for rare words. Experimental results show that the proposed approach
helps the baseline model significantly outperform state-of-the-art baselines on
various public benchmarks.

29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio