Abstract: Traditional approaches to dialogue segmentation perform quite well on synthetic or short dialogues but suffer when dealing with long, noisy dialogs. In addition, such methods require careful tuning of hyperparameters. We propose to leverage a novel approach that is based on dialogue summaries. Experiments on different datasets showed that the new approach outperforms popular SotA algorithms in unsupervised topic segmentation and requires less setup.
The source code is available at
https://anonymous.4open.science/r/unsupervised-summary-based-segmentation.
Paper Type: short
Research Area: Dialogue and Interactive Systems
Contribution Types: Model analysis & interpretability, NLP engineering experiment
Languages Studied: english
0 Replies
Loading