Summary of Diner: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference, by Jialong Wu et al.
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
by Jialong Wu, Linhai Zhang, Deyu Zhou, Guoqiang Xu
First submitted to arxiv on: 2 Mar 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 A novel framework based on multi-variable causal inference is proposed for debiasing neural-based aspect-based sentiment analysis (ABSA) models, which are prone to learn spurious correlations from annotation biases. The framework tackles different types of biases using various causal intervention methods. For the review branch, backdoor adjustment intervention is employed to model indirect confounding from context, while counterfactual reasoning is adopted for debiasing the aspect branch’s direct correlation with labels. Experimental results demonstrate the effectiveness of the proposed method compared to various baselines on two widely used real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve how computers analyze opinions about specific things in reviews. Right now, these computer models are easily tricked into thinking certain patterns are important when they’re not. The researchers came up with a new way to make the models less gullible by using special math techniques that take into account where the biases come from. They tested their idea on two big datasets and showed it works better than other methods. |
Keywords
» Artificial intelligence » Inference
