Summary of Mare: Multi-aspect Rationale Extractor on Unsupervised Rationale Extraction, by Han Jiang et al.
MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction
by Han Jiang, Junwen Duan, Zhe Qu, Jianxin Wang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to unsupervised rationale extraction is proposed, aiming to extract text snippets that support model predictions without explicit annotation. The method, dubbed Multi-Aspect Rationale Extractor (MARE), leverages beneficial internal correlations between aspects to improve multi-aspect reasoning. A key innovation is the Multi-Aspect Multi-Head Attention (MAMHA) mechanism, which encodes multiple text chunks simultaneously using hard deletion. To further enhance performance, special tokens are prepended to each text chunk corresponding to a specific aspect, and multi-task training is deployed. Experimental results on two benchmarks demonstrate state-of-the-art performance for MARE, with ablation studies highlighting the method’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative approach to unsupervised rationale extraction. It aims to help machines understand why they made certain predictions by finding relevant text snippets. The new method, called Multi-Aspect Rationale Extractor (MARE), looks at how different parts of a text relate to each other. This helps it make better decisions about what text is important for explaining model predictions. The researchers tested MARE on two big datasets and found that it outperformed previous methods. |
Keywords
» Artificial intelligence » Multi head attention » Multi task » Unsupervised
