  Dialogue Topic Segmentation (DTS) plays an essential role in a variety of
dialogue modeling tasks. Previous DTS methods either focus on semantic
similarity or dialogue coherence to assess topic similarity for unsupervised
dialogue segmentation. However, the topic similarity cannot be fully identified
via semantic similarity or dialogue coherence. In addition, the unlabeled
dialogue data, which contains useful clues of utterance relationships, remains
underexploited. In this paper, we propose a novel unsupervised DTS framework,
which learns topic-aware utterance representations from unlabeled dialogue data
through neighboring utterance matching and pseudo-segmentation. Extensive
experiments on two benchmark datasets (i.e., DialSeg711 and Doc2Dial)
demonstrate that our method significantly outperforms the strong baseline
methods. For reproducibility, we provide our code and data
at:https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial-start.

Computation and Language;Artificial Intelligence