Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Matching Models with Hierarchical Contextualized
  Representations for Multi-turn Response Selection   In this paper, we study context-response matching with pre-trained
contextualized representations for multi-turn response selection in
retrieval-based chatbots. Existing models, such as Cove and ELMo, are trained
with limited context (often a single sentence or paragraph), and may not work
well on multi-turn conversations, due to the hierarchical nature, informal
language, and domain-specific words. To address the challenges, we propose
pre-training hierarchical contextualized representations, including contextual
word-level and sentence-level representations, by learning a dialogue
generation model from large-scale conversations with a hierarchical
encoder-decoder architecture. Then the two levels of representations are
blended into the input and output layer of a matching model respectively.
Experimental results on two benchmark conversation datasets indicate that the
proposed hierarchical contextualized representations can bring significantly
and consistently improvement to existing matching models for response
selection.

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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: solv-int