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

TL;DR

This paper introduces a sequential matching framework (SMF) for multi-turn response selection in retrieval-based chatbots, improving the ability to capture important context information and relationships among utterances.

Contribution

The paper proposes a novel SMF that models interactions between each utterance and response, and accumulates matching information with an RNN, outperforming existing methods.

Findings

Sequential models outperform traditional fixed-vector approaches.

Proposed models achieve significant improvements on public datasets.

Models are interpretable with visualizations revealing context information usage.

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.