Dialogue Topic Segmentation via Parallel Extraction Network with Neighbor Smoothing (original) (raw)

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Dialogue topic segmentation is a challenging task in which dialogues are split into segments with pre-defined topics. Existing works on topic segmentation adopt a two-stage paradigm, including text segmentation and segment labeling. However, such methods tend to focus on the local context in segmentation, and the inter-segment dependency is not well captured. Besides, the ambiguity and labeling noise in dialogue segment bounds bring further challenges to existing models. In this work, we propose the Parallel Extraction Network with Neighbor Smoothing (PEN-NS) to address the above issues. Specifically, we propose the parallel extraction network to perform segment extractions, optimizing the bipartite matching cost of segments to capture inter-segment dependency. Furthermore, we propose neighbor smoothing to handle the segment-bound noise and ambiguity. Experiments on a dialoguebased and a document-based topic segmentation dataset show that PEN-NS outperforms state-the-of-art models significantly. CCS CONCEPTS • Computing methodologies → Discourse, dialogue and pragmatics; Information extraction. * The work was done while the author was an intern at Meituan.

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