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        <title>Detecting Context Dependent Messages in a Conversational Environment</title>
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        <namePart type="given">Chaozhuo</namePart>
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    <abstract>While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process. The task is challenging, as messages in a conversational environment are short and informal, and evidence that can indicate a message is context dependent is scarce. After a study of social conversation data crawled from the web, we observed that some characteristics estimated from the responses of messages are discriminative for identifying context dependent messages. With the characteristics as weak supervision, we propose using a Long Short Term Memory (LSTM) network to learn a classifier. Our method carries out text representation and classifier learning in a unified framework. Experimental results show that the proposed method can significantly outperform baseline methods on accuracy of classification.</abstract>
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%0 Conference Proceedings
%T Detecting Context Dependent Messages in a Conversational Environment
%A Li, Chaozhuo
%A Wu, Yu
%A Wu, Wei
%A Xing, Chen
%A Li, Zhoujun
%A Zhou, Ming
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F li-etal-2016-detecting
%X While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process. The task is challenging, as messages in a conversational environment are short and informal, and evidence that can indicate a message is context dependent is scarce. After a study of social conversation data crawled from the web, we observed that some characteristics estimated from the responses of messages are discriminative for identifying context dependent messages. With the characteristics as weak supervision, we propose using a Long Short Term Memory (LSTM) network to learn a classifier. Our method carries out text representation and classifier learning in a unified framework. Experimental results show that the proposed method can significantly outperform baseline methods on accuracy of classification.
%U https://aclanthology.org/C16-1187
%P 1990-1999

Markdown (Informal)

[Detecting Context Dependent Messages in a Conversational Environment](https://aclanthology.org/C16-1187) (Li et al., COLING 2016)

• Detecting Context Dependent Messages in a Conversational Environment (Li et al., COLING 2016)

ACL

• Chaozhuo Li, Yu Wu, Wei Wu, Chen Xing, Zhoujun Li, and Ming Zhou. 2016. Detecting Context Dependent Messages in a Conversational Environment. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1990–1999, Osaka, Japan. The COLING 2016 Organizing Committee.