Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Logical Parsing from Natural Language Based on a Neural Translation
  Model   Semantic parsing has emerged as a significant and powerful paradigm for
natural language interface and question answering systems. Traditional methods
of building a semantic parser rely on high-quality lexicons, hand-crafted
grammars and linguistic features which are limited by applied domain or
representation. In this paper, we propose a general approach to learn from
denotations based on Seq2Seq model augmented with attention mechanism. We
encode input sequence into vectors and use dynamic programming to infer
candidate logical forms. We utilize the fact that similar utterances should
have similar logical forms to help reduce the searching space. Under our
learning policy, the Seq2Seq model can learn mappings gradually with noises.
Curriculum learning is adopted to make the learning smoother. We test our
method on the arithmetic domain which shows our model can successfully infer
the correct logical forms and learn the word meanings, compositionality and
operation orders simultaneously.

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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: eess
0
The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex