Summary of An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting, by Xia Hou et al.
An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting
by Xia Hou, Qifeng Li, Tongliang Li
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Discourse Rewriting Topic Segmentation Model (UR-DTS) tackles dialogue topic segmentation for unsupervised modeling tasks by combining Utterance Rewriting (UR) with an unsupervised learning algorithm. This novel approach efficiently utilizes cues in unlabeled dialogs by rewriting them to recover co-referents and omitted words, leading to improved accuracy. The model significantly outperforms existing unsupervised methods on DialSeg711 and Doc2Dial benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of understanding conversations without labeled data. It uses a technique called Utterance Rewriting (UR) to rewrite conversations in a way that makes it easier for computers to understand the topic being discussed. This helps improve the accuracy of identifying topics in conversations, making it more useful for tasks like chatbots and language translation. |
Keywords
» Artificial intelligence » Discourse » Translation » Unsupervised
