Summary of A Zero-shot Open-vocabulary Pipeline For Dialogue Understanding, by Abdulfattah Safa et al.
A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding
by Abdulfattah Safa, Gözde Gül Şahin
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 system integrates domain classification and Dialogue State Tracking (DST) in a single pipeline, allowing it to adapt dynamically without relying on fixed slot values defined in an ontology. The approach reformulates DST as a question-answering task for less capable models and employs self-refining prompts for more adaptable ones. This zero-shot, open-vocabulary system demonstrates up to 20% better Joint Goal Accuracy (JGA) compared to previous methods on datasets like Multi-WOZ 2.1, with up to 90% fewer requests to the Large Language Model API. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand what people are saying in conversations and respond accordingly. Most current systems struggle when they encounter things they’ve never seen before, but this system can adapt to new situations without needing a lot of training data. It does this by turning Dialogue State Tracking into a question-answering task that less powerful computers can handle, and using self-improving prompts for more advanced models. This allows the system to work in real-life conversations without requiring extensive computational resources. |
Keywords
» Artificial intelligence » Classification » Large language model » Question answering » Tracking » Zero shot
