  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.
