Sequential Matching Model for End-to-end Multi-turn Response Selection

Abstract

Multi-turn conversation understanding is an important challenge for building intelligent dialogue systems, and end-to-end multi-turn response selection is one of the major tasks. Previous state-of-the-art models used hierarchy-based (utterance-level and token-level) neural networks to explicitly model the interactions among the different turns' utterances for context modeling. In this paper, we demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. We investigate a sequential matching model based only on chain sequence for multi-turn response selection. The proposed model outperforms all previous models, including previous state-of-the-art hierarchy-based models, and achieves new state-of-the-art performances on two large-scale public multi-turn response selection benchmark datasets.