  In the retrieval-based multi-turn dialogue modeling, it remains a challenge
to select the most appropriate response according to extracting salient
features in context utterances. As a conversation goes on, topic shift at
discourse-level naturally happens through the continuous multi-turn dialogue
context. However, all known retrieval-based systems are satisfied with
exploiting local topic words for context utterance representation but fail to
capture such essential global topic-aware clues at discourse-level. Instead of
taking topic-agnostic n-gram utterance as processing unit for matching purpose
in existing systems, this paper presents a novel topic-aware solution for
multi-turn dialogue modeling, which segments and extracts topic-aware
utterances in an unsupervised way, so that the resulted model is capable of
capturing salient topic shift at discourse-level in need and thus effectively
track topic flow during multi-turn conversation. Our topic-aware modeling is
implemented by a newly proposed unsupervised topic-aware segmentation algorithm
and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each
topic segment with the response in a dual cross-attention way. Experimental
results on three public datasets show TADAM can outperform the state-of-the-art
method, especially by 3.3% on E-commerce dataset that has an obvious topic
shift.
