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Dialog Context Language Modeling with Recurrent Neural Networks

Abstract: 

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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Paper Details

Authors:
Bing Liu, Ian Lane
Submitted On:
9 March 2017 - 4:59pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Bing Liu
Paper Code:
HLT-P1.3
Document Year:
2017
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Dialog Context Language Modeling with Recurrent Neural Networks

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[1] Bing Liu, Ian Lane, "Dialog Context Language Modeling with Recurrent Neural Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1730. Accessed: Dec. 17, 2017.
@article{1730-17,
url = {http://sigport.org/1730},
author = {Bing Liu; Ian Lane },
publisher = {IEEE SigPort},
title = {Dialog Context Language Modeling with Recurrent Neural Networks},
year = {2017} }
TY - EJOUR
T1 - Dialog Context Language Modeling with Recurrent Neural Networks
AU - Bing Liu; Ian Lane
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1730
ER -
Bing Liu, Ian Lane. (2017). Dialog Context Language Modeling with Recurrent Neural Networks. IEEE SigPort. http://sigport.org/1730
Bing Liu, Ian Lane, 2017. Dialog Context Language Modeling with Recurrent Neural Networks. Available at: http://sigport.org/1730.
Bing Liu, Ian Lane. (2017). "Dialog Context Language Modeling with Recurrent Neural Networks." Web.
1. Bing Liu, Ian Lane. Dialog Context Language Modeling with Recurrent Neural Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1730