Summary of A Dataset Of Open-domain Question Answering with Multiple-span Answers, by Zhiyi Luo et al.
A Dataset of Open-Domain Question Answering with Multiple-Span Answers
by Zhiyi Luo, Yingying Zhang, Shuyun Luo, Ying Zhao, Wentao Lyu
First submitted to arxiv on: 15 Feb 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 paper addresses the lack of publicly available benchmark datasets for Chinese multi-span question answering (MSQA), which is crucial for real-world applications that require extracting multiple pieces of information from text to answer complex questions. The existing datasets are biased towards collecting factoid questions, overlooking those requiring descriptive answers. To overcome this limitation, the authors present CLEAN, a comprehensive Chinese MSQA dataset that covers a wide range of open-domain subjects and requires descriptive answers. The dataset is designed to challenge established models in the field, and experimental results show its characteristics and difficulty. The authors provide baselines for CLEAN using established models from relevant literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset called CLEAN, which helps computers answer complex questions by finding multiple pieces of information in Chinese text. This is important because it can be used in real-world applications like chatbots or virtual assistants. Right now, there aren’t many datasets available for this task in Chinese, so the authors made one to help researchers work on it. The dataset has a lot of different types of questions and answers, and it’s designed to challenge current computer models. |
Keywords
» Artificial intelligence » Question answering
