Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

Abstract

In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of encoding sentences hierarchically and generating more descriptive representations by aggregating with an attention mechanism, is designed. Finally, the bidirectional interactions between whole multi-turn contexts and response candidates are calculated to derive the matching information between them. Experiments on four public datasets show that IMN outperforms the baseline models on all metrics, achieving a new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.

Accepted by CIKM 2019

References in corpus (3)

• Enhanced LSTM for Natural Language Inference
• Modeling Multi-turn Conversation with Deep Utterance Aggregation
• A Compare-Aggregate Model for Matching Text Sequences

Cited by in corpus (4)

• Dialogue-oriented Pre-training
• Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
• Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems
• Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing