Summary of Leveraging Label Semantics and Meta-label Refinement For Multi-label Question Classification, by Shi Dong and Xiaobei Niu and Rui Zhong and Zhifeng Wang and Mingzhang Zuo
Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification
by Shi Dong, Xiaobei Niu, Rui Zhong, Zhifeng Wang, Mingzhang Zuo
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 RR2QC, a novel Retrieval Reranking method to multi-label Question Classification, is introduced in this paper. The approach leverages label semantics and meta-label refinement to address the challenges of fine-grained knowledge labels overlapping or sharing similarities. RR2QC improves pre-training by utilizing semantic relationships within and across label groups, and introduces a class center learning task to align questions with label semantics during downstream training. Additionally, RR2QC decomposes labels into meta-labels and uses a meta-label classifier to rerank the retrieved label sequences. Experimental results show that RR2QC outperforms existing methods in Precision@K and F1 scores across multiple educational datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RR2QC is a new way to better understand and predict questions’ answers by using words’ meanings and relationships. It works by first learning how to group similar labels together, then using those groups to improve its ability to identify the correct answer. This helps when there are many possible answers because it can focus on the most relevant ones. RR2QC is tested on several educational datasets and shows better results than other methods. |
Keywords
» Artificial intelligence » Classification » Precision » Semantics
