Summary of Correct After Answer: Enhancing Multi-span Question Answering with Post-processing Method, by Jiayi Lin et al.
Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method
by Jiayi Lin, Chenyang Zhang, Haibo Tong, Dongyu Zhang, Qingqing Hong, Bingxuan Hou, Junli Wang
First submitted to arxiv on: 22 Oct 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 proposed Answering-Classifying-Correcting (ACC) framework addresses the issue of prior work in Multi-Span Question Answering (MSQA), which primarily focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions. The ACC framework employs a post-processing strategy to handle incorrect predictions, first classifying predictions into three types and excluding “wrong predictions”, then modifying “partially correct predictions” using a corrector. Experimental results on several MSQA datasets show that the ACC framework significantly improves Exact Match (EM) scores and efficiently reduces the number of incorrect predictions, improving prediction quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed Answering-Classifying-Correcting (ACC) framework helps machines better answer questions by fixing mistakes. Most previous approaches focused on making models predict more correct answers, but they didn’t consider when models make mistakes. The ACC framework is designed to handle these mistakes by first identifying and removing wrong predictions, then correcting partially correct ones. This results in much improved question-answering performance. |
Keywords
» Artificial intelligence » Question answering
