Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Improvements for Exploiting Dependency Trees in Neural
  Semantic Parsing   The dependency tree of a natural language sentence can capture the
interactions between semantics and words. However, it is unclear whether those
methods which exploit such dependency information for semantic parsing can be
combined to achieve further improvement and the relationship of those methods
when they combine. In this paper, we examine three methods to incorporate such
dependency information in a Transformer based semantic parser and empirically
study their combinations. We first replace standard self-attention heads in the
encoder with parent-scaled self-attention (PASCAL) heads, i.e., the ones that
can attend to the dependency parent of each token. Then we concatenate
syntax-aware word representations (SAWRs), i.e., the intermediate hidden
representations of a neural dependency parser, with ordinary word embedding to
enhance the encoder. Later, we insert the constituent attention (CA) module to
the encoder, which adds an extra constraint to attention heads that can better
capture the inherent dependency structure of input sentences. Transductive
ensemble learning (TEL) is used for model aggregation, and an ablation study is
conducted to show the contribution of each method. Our experiments show that CA
is complementary to PASCAL or SAWRs, and PASCAL + CA provides state-of-the-art
performance among neural approaches on ATIS, GEO, and JOBS.

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The categories of this paper are: solv-int
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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: cond-mat
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: math