Summary of Reinforcement Learning For Conversational Question Answering Over Knowledge Graph, by Mi Wu
Reinforcement Learning for Conversational Question Answering over Knowledge Graph
by Mi Wu
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper proposes a reinforcement learning-based approach to conversational question answering over law knowledge bases (KBs), aiming to improve the accuracy of answering multi-turn natural language questions about law. The existing models assume clear input questions that perfectly reflect user intentions, but in reality, questions are often noisy and inexplicit. To address this issue, the authors develop a reinforcement learning agent that learns to find answers based on input questions and conversation history, even when questions are unclear. The proposed method is evaluated on several real-world datasets, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make it easier for computers to answer complex law-related questions by improving how they understand what people are asking. Right now, computer models that answer these questions assume the person asking the question is very clear about what they want to know. But in real life, people might not be as clear or specific. To fix this, the authors came up with a new way for computers to learn from examples and figure out what people really mean when they ask a question. They tested their idea on some big datasets and found that it worked really well. |
Keywords
» Artificial intelligence » Question answering » Reinforcement learning
