Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dialog Context Language Modeling with Recurrent Neural Networks   In this work, we propose contextual language models that incorporate dialog
level discourse information into language modeling. Previous works on
contextual language model treat preceding utterances as a sequence of inputs,
without considering dialog interactions. We design recurrent neural network
(RNN) based contextual language models that specially track the interactions
between speakers in a dialog. Experiment results on Switchboard Dialog Act
Corpus show that the proposed model outperforms conventional single turn based
RNN language model by 3.3% on perplexity. The proposed models also demonstrate
advantageous performance over other competitive contextual language models.

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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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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: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
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The categories of this paper are: physics
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The categories of this paper are: eess