Multi-turn Dialogue Comprehension from a Topic-aware Perspective

TL;DR

This paper introduces a topic-aware approach to multi-turn dialogue comprehension, using unsupervised segmentation and a novel dual-attention model to improve understanding and response selection in dialogue systems.

Contribution

It proposes a new unsupervised dialogue segmentation method and a topic-aware dual-attention network, advancing dialogue comprehension by incorporating topic modeling.

Findings

Significant performance improvements on three public benchmarks.

Effective unsupervised segmentation of dialogue into topic-focused segments.

Enhanced response selection accuracy with the TADAM model.

Abstract

Dialogue related Machine Reading Comprehension requires language models to effectively decouple and model multi-turn dialogue passages. As a dialogue development goes after the intentions of participants, its topic may not keep constant through the whole passage. Hence, it is non-trivial to detect and leverage the topic shift in dialogue modeling. Topic modeling, although has been widely studied in plain text, deserves far more utilization in dialogue reading comprehension. This paper proposes to model multi-turn dialogues from a topic-aware perspective. We start with a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. Then we use these fragments as topic-aware language processing units in further dialogue comprehension. On one hand, the split segments indict specific topics rather than mixed intentions, thus showing convenient on in-domain topic detection and location. For this task, we design a clustering system with a self-training auto-encoder, and we build two constructed datasets for evaluation. On the other hand, the split segments are an appropriate element of multi-turn dialogue response selection. For this purpose, we further present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements and matches response candidates with a dual cross-attention. Empirical studies on three public benchmarks show great improvements over baselines. Our work continues the previous studies on document topic, and brings the dialogue modeling to a novel topic-aware perspective with exhaustive experiments and analyses.