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Dialogue topic segmentation is critical in several dialogue modelingproblems. However, popular unsupervised approaches only exploit surfacefeatures in assessing topical coherence among utterances. In this work, weaddress this limitation by leveraging supervisory signals from theutterance-pair coherence scoring task. First, we present a simple yet effectivestrategy to generate a training corpus for utterance-pair coherence scoring.Then, we train a BERT-based neural utterance-pair coherence model with theobtained training corpus. Finally, such model is used to measure the topicalrelevance between utterances, acting as the basis of the segmentationinference. Experiments on three public datasets in English and Chinesedemonstrate that our proposal outperforms the state-of-the-art baselines.

Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring