Summary of Using Domain Knowledge to Guide Dialog Structure Induction Via Neural Probabilistic Soft Logic, by Connor Pryor et al.
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
by Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 addresses the problem of Dialog Structure Induction (DSI), which involves inferring latent dialog states and their transitions. Existing approaches are limited by relying solely on data-driven models, underperforming when faced with noisy or limited training data, or struggling to generalize to new domains. The authors propose Neural Probabilistic Soft Logic Dialogue Structure Induction (NEUPSL DSI), a neural-symbolic approach that injects symbolic knowledge into the latent space of a generative model. They demonstrate the effectiveness of this approach on three datasets and in both unsupervised and semi-supervised settings, showcasing improved performance over canonical baselines. The results highlight the potential benefits of incorporating symbolic knowledge into DSI models for better hidden representation quality, few-shot learning, and out-of-domain generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on understanding how computers can analyze conversations to understand their meaning. Right now, computers struggle to learn from limited or noisy conversation data. They also have trouble applying what they’ve learned to new situations. The authors developed a new approach that combines computer learning with human knowledge. This helps the computer better understand conversations and make more accurate predictions. They tested this approach on several datasets and found it worked better than previous methods. This breakthrough could lead to more advanced AI systems that can better understand and respond to human conversations. |
Keywords
* Artificial intelligence * Domain generalization * Few shot * Generative model * Latent space * Semi supervised * Unsupervised
