Summary of Intent-driven In-context Learning For Few-shot Dialogue State Tracking, by Zihao Yi and Zhe Xu and Ying Shen
Intent-driven In-context Learning for Few-shot Dialogue State Tracking
by Zihao Yi, Zhe Xu, Ying Shen
First submitted to arxiv on: 4 Dec 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 This paper tackles the challenge of dialogue state tracking (DST) in task-oriented dialogue systems, where users’ inputs may contain implicit information that poses difficulties for DST tasks. The authors propose Intent-driven In-context Learning for Few-shot DST (IDIC-DST), which extracts user intent and augments dialogue information to track states more effectively. They also mask noisy information from DST data and retrieve similar examples using a pre-trained large language model. Experimental results demonstrate IDIC-DST achieves state-of-the-art performance on MultiWOZ 2.1 and 2.4 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand conversations better by recognizing what people want to talk about. It’s like trying to figure out what someone is saying when they’re hinting at something without directly saying it. The authors create a new way to make this process easier, using special techniques to learn from small amounts of data. This can help improve how well computers respond to users in certain situations. |
Keywords
» Artificial intelligence » Few shot » Large language model » Mask » Tracking
