Summary of Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-based Sentiment Analysis Evaluation, by Soyoung Yang et al.
Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation
by Soyoung Yang, Hojun Cho, Jiyoung Lee, Sohee Yoon, Edward Choi, Jaegul Choo, Won Ik Cho
First submitted to arxiv on: 13 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 pipeline expands existing evaluation sets in aspect-based sentiment analysis (ABSA) by adding alternative valid terms for aspects and opinions. This approach allows for a more equitable assessment of language models, accommodating multiple-answer candidates to improve human agreement (up to 10% improvement in Kendall’s Tau score). Experimental results demonstrate the capabilities of large language models (LLMs) in ABSA tasks, which is concealed by single-answer ground truth sets. The work contributes to developing a flexible evaluation framework for ABSA, embracing diverse surface forms to extract spans in a cost-effective and reproducible manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to evaluate language models that do sentiment analysis. It does this by adding more possible answers to existing test sets, so that different models can be compared fairly. This helps us understand what language models are good at, and what they’re not as good at. The method is useful for a task called aspect-based sentiment analysis (ABSA), which tries to figure out the sentiment (positive or negative) towards specific things in text. |
