AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine

We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models. AliMe Chat uses an attentive Seq2Seq based rerank model to optimize the joint results. Extensive experiments show our engine outperforms both IR and generation based models. We launch AliMe Chat for a real-world industrial application and observe better results than another public chatbot.

[1]  Peter Stone,et al.  Cobot in LambdaMOO: A Social Statistics Agent , 2000, AAAI/IAAI.

[2]  Oliver Lemon,et al.  A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information , 2013, SIGDIAL Conference.

[3]  Hang Li,et al.  Neural Responding Machine for Short-Text Conversation , 2015, ACL.

[4]  Yoshua Bengio,et al.  On Using Very Large Target Vocabulary for Neural Machine Translation , 2014, ACL.

[5]  Xiang Li,et al.  Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems , 2016, ArXiv.

[6]  Quoc V. Le,et al.  A Neural Conversational Model , 2015, ArXiv.

[7]  David Vandyke,et al.  A Network-based End-to-End Trainable Task-oriented Dialogue System , 2016, EACL.

[8]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[9]  Hang Li,et al.  An Information Retrieval Approach to Short Text Conversation , 2014, ArXiv.

[10]  Rui Yan,et al.  Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System , 2016, SIGIR.

[11]  Jianfeng Gao,et al.  A Neural Network Approach to Context-Sensitive Generation of Conversational Responses , 2015, NAACL.

[12]  Tomas Mikolov,et al.  Enriching Word Vectors with Subword Information , 2016, TACL.

[13]  Joelle Pineau,et al.  Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models , 2015, AAAI.

[14]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[15]  Hugo Zaragoza,et al.  The Probabilistic Relevance Framework: BM25 and Beyond , 2009, Found. Trends Inf. Retr..

[16]  Jianfeng Gao,et al.  A Diversity-Promoting Objective Function for Neural Conversation Models , 2015, NAACL.

[17]  Zhoujun Li,et al.  DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents , 2016, ACL.

[18]  David Vandyke,et al.  Multi-domain Dialog State Tracking using Recurrent Neural Networks , 2015, ACL.

[19]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[20]  Matthew Henderson,et al.  Machine Learning for Dialog State Tracking: A Review , 2015 .

[21]  Geoffrey Zweig,et al.  End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning , 2016, ArXiv.

[22]  Alan Ritter,et al.  Data-Driven Response Generation in Social Media , 2011, EMNLP.