Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Multi-turn Conversation with Deep Utterance Aggregation   Multi-turn conversation understanding is a major challenge for building
intelligent dialogue systems. This work focuses on retrieval-based response
matching for multi-turn conversation whose related work simply concatenates the
conversation utterances, ignoring the interactions among previous utterances
for context modeling. In this paper, we formulate previous utterances into
context using a proposed deep utterance aggregation model to form a
fine-grained context representation. In detail, a self-matching attention is
first introduced to route the vital information in each utterance. Then the
model matches a response with each refined utterance and the final matching
score is obtained after attentive turns aggregation. Experimental results show
our model outperforms the state-of-the-art methods on three multi-turn
conversation benchmarks, including a newly introduced e-commerce dialogue
corpus.

3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg