Abstract

Multi-turn response selection has been extensively studied and applied to many real-world applications in recent years. However, current methods typically model the interactions between multi-turn utterances and candidate responses with iterative approaches, which is not practical as the turns of conversations vary. Besides, some latent features, such as user intent and conversation topic, are under-discovered in existing works. In this work, we propose Intra-/Inter-Interaction Network (I3) with latent interaction modeling to comprehensively model multi-level interactions between the utterance context and the response. In specific, we first encode the intra- and inter-utterance interaction with the given response from both individual utterance and the overall utterance context. Then we develop a latent multi-view subspace clustering module to model the latent interaction between the utterance and response. Experimental results show that the proposed method substantially and consistently outperforms existing state-of-the-art methods on three multi-turn response selection benchmark datasets.

Highlights

  • Recent years have witnessed many successful real-world applications on chatbots and AI assistants, such as the XiaoIce (Shum et al, 2018) from Microsoft and the E-commerce assistant AliMe (Li et al, 2017) from Alibaba Group, which owe to the extensive researches on dialogue systems

  • The main contributions of this work are as follows: (1) We propose a novel multi-turn response selection model, Intra-/Inter-Interaction Network (I3), to capture the multi-level matching information by modeling the multi-turn conversations as a hierarchical structure; (2) We develop two kinds of latent multi-view subspace clustering module to model the latent feature coherency between the utterance and response; (3) Experimental results show that the proposed method substantially and consistently outperforms existing state-of-the-art methods on three multi-turn dialogue benchmark datasets

  • In addition to the intra- and inter-utterance interactions, we develop a latent multi-view subspace clustering approaches for the representational learning of latent features in the dialog content to capture the latent interaction between the utterance context and the candidate response, in which the utterance context and the response are regarded as two different views of dialog content

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Summary

Introduction

Recent years have witnessed many successful real-world applications on chatbots and AI assistants, such as the XiaoIce (Shum et al, 2018) from Microsoft and the E-commerce assistant AliMe (Li et al, 2017) from Alibaba Group, which owe to the extensive researches on dialogue systems. We focus on the problem of multi-turn response selection for retrieval-based dialogue systems, which aims at selecting appropriate responses from a set of candidates as the reply for the given multi-turn utterances. Measuring the matching degree between the utterance context and the candidate response is the core of multi-turn response selection task. Recent works develop a variety of interaction model to enhance the utterance-response interaction from a broader (Zhou et al, 2018b; Tao et al, 2019a) or deeper perspective (Tao et al, 2019b; Wang et al, 2019; Yuan et al, 2019). Empirical evidences show that iterative architectures achieve state-of-the-art performance on multi-turn response selection, such as interactionover-interaction (Tao et al, 2019b), iterated attentive matching (Wang et al, 2019), and multi-hop selector (Yuan et al, 2019)

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