Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Composing Answer from Multi-spans for Reading Comprehension   This paper presents a novel method to generate answers for non-extraction
machine reading comprehension (MRC) tasks whose answers cannot be simply
extracted as one span from the given passages. Using a pointer network-style
extractive decoder for such type of MRC may result in unsatisfactory
performance when the ground-truth answers are given by human annotators or
highly re-paraphrased from parts of the passages. On the other hand, using
generative decoder cannot well guarantee the resulted answers with well-formed
syntax and semantics when encountering long sentences. Therefore, to alleviate
the obvious drawbacks of both sides, we propose an answer making-up method from
extracted multi-spans that are learned by our model as highly confident
$n$-gram candidates in the given passage. That is, the returned answers are
composed of discontinuous multi-spans but not just one consecutive span in the
given passages anymore. The proposed method is simple but effective: empirical
experiments on MS MARCO show that the proposed method has a better performance
on accurately generating long answers, and substantially outperforms two
competitive typical one-span and Seq2Seq baseline decoders.

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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph