Enhancing Multi-turn Dialogue Modeling with Intent Information for E-Commerce Customer Service

Nowadays, it is a heated topic for many industries to build intelligent conversational bots for customer service. A critical solution to these dialogue systems is to understand the diverse and changing intents of customers accurately. However, few studies have focused on the intent information due to the lack of large-scale dialogue corpus with intent labelled. In this paper, we propose to leverage intent information to enhance multi-turn dialogue modeling. First, we construct a large-scale Chinese multi-turn E-commerce conversation corpus with intent labelled, namely E-IntentConv, which covers 289 fine-grained intents in after-sales domain. Specifically, we utilize the attention mechanism to extract Intent Description Words (IDW) for representing each intent explicitly. Then, based on E-IntentConv, we propose to integrate intent information for both retrieval-based model and generation-based model to verify its effectiveness for multi-turn dialogue modeling. Experimental results show that extra intent information is useful for improving both response selection and generation tasks.

[1]  Hao Wang,et al.  A Dataset for Research on Short-Text Conversations , 2013, EMNLP.

[2]  Zhoujun Li,et al.  Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots , 2016, ArXiv.

[3]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

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

[5]  Sungjin Lee,et al.  Jointly Optimizing Diversity and Relevance in Neural Response Generation , 2019, NAACL.

[6]  Hai Zhao,et al.  Modeling Multi-turn Conversation with Deep Utterance Aggregation , 2018, COLING.

[7]  Zhen-Hua Ling,et al.  Enhanced LSTM for Natural Language Inference , 2016, ACL.

[8]  Dongyan Zhao,et al.  An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems , 2018, IJCAI.

[9]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[10]  Xuanjing Huang,et al.  Generating Responses with a Specific Emotion in Dialog , 2019, ACL.

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

[12]  Xueqi Cheng,et al.  ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation , 2019, ACL.

[13]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[14]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[15]  Zhaohui Zheng,et al.  Stochastic gradient boosted distributed decision trees , 2009, CIKM.

[16]  Joelle Pineau,et al.  The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems , 2015, SIGDIAL Conference.

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

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

[19]  Ying Chen,et al.  Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network , 2018, ACL.

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

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

[22]  Ming-Wei Chang,et al.  A Knowledge-Grounded Neural Conversation Model , 2017, AAAI.

[23]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .