A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots

Abstract

We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. The analysis motivates us to propose a new matching framework that can sufficiently carry the important information in contexts to matching and model the relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interacts with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) which models the relationships among the utterances. The context-response matching is finally calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experimental results show that both models can significantly outperform the state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage the important information in contexts for matching.

Submitted to Computational Linguistics

References in corpus (21)

• Adam: A Method for Stochastic Optimization
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• A Neural Conversational Model
• Enhanced LSTM for Natural Language Inference
• Convolutional Neural Networks for Sentence Classification
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• Reasoning about Entailment with Neural Attention
• Deep Reinforcement Learning for Dialogue Generation
• LSTM-based Deep Learning Models for Non-factoid Answer Selection
• Neural Responding Machine for Short-Text Conversation
• Adversarial Learning for Neural Dialogue Generation
• A Persona-Based Neural Conversation Model
• An Information Retrieval Approach to Short Text Conversation
• Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
• Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
• Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
• A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
• Learning Natural Language Inference with LSTM
• ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
• Syntax-based Deep Matching of Short Texts
• Modelling Interaction of Sentence Pair with coupled-LSTMs

Cited by in corpus (1)

• Retrieval-Enhanced Adversarial Training for Neural Response Generation