基于话语重写的无监督对话主题分割算法

Dialogue Topic Segmentation(DTS) task aims to automatically divide a multi-turn conversation into different topic segments, enabling more precise understanding and processing of dialogue content. DTS plays an important role in dialogue modeling tasks. Traditional DTS methods primarily rely on semantic similarity and dialogue coherence to perform unsupervised topic segmentation, but these features are often insufficient to fully capture complex topic transitions in conversations, and unannotated dia-logue data has not been fully explored and utilized. To address this issue, recent DTS methods employ adjacent utterance matching and pseudo-segmentation to learn topic-aware representations from dialogue data, further extracting useful cues from unannotated dialogues. However, common phenomena such as coreference and ellipsis in multi-turn dialogues may affect the calculation of semantic similarity, thereby weakening the accuracy of adjacent utterance matching. To solve this problem and fully leverage the useful cues in dialogue relationships, this study proposes a novel unsupervised DTS method that combines utterance rewriting (UR) techniques with unsupervised learning algorithms. This approach rewrites coreferential and elliptical expressions in the dialogue to restore them to their complete forms, better capturing the thematic cues in the conversation. Experimental results show that the proposed utterance rewriting topic segmentation model (UR-DTS) significantly improves topic segmentation accuracy, achieving state-of-the-art performance. On the DialSeg711 dataset, the error rate P_k and WinDiff(WD) improves by approximately 6 percentage point, reaching 11.42% and 12.97%, respectively. On the more complex Doc2Dial dataset, P_k and WD improve by 3 percentage point and 2 percentage point, reaching 35.17% and 38.49%. These results demonstrate that UR-DTS has a significant advantage in capturing topic transitions in conversations and shows greater potential for leveraging unannotated dialogue data.