Summary of Improving Commonsense Bias Classification by Mitigating the Influence Of Demographic Terms, By Jinkyu Lee et al.
Improving Commonsense Bias Classification by Mitigating the Influence of Demographic Terms
by JinKyu Lee, Jihie Kim
First submitted to arxiv on: 11 Jun 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 This paper tackles a crucial challenge in Natural Language Processing (NLP), where the presence of demographic terms in commonsense knowledge can compromise the performance of NLP models. To address this issue, three methods are proposed to enhance the performance of a commonsense polarization classifier: hierarchical generalization of demographic terms, threshold-based augmentation, and integration of these two approaches (IHTA). The first method replaces demographic terms with more general ones based on a term hierarchy ontology, aiming to mitigate specific terms’ influence. The second method measures demographic terms’ polarization by comparing model predictions when these terms are masked versus unmasked, then augments commonsense sentences containing high-polarization terms by replacing their predicates with ChatGPT-generated synonyms. The third method combines threshold-based augmentation and hierarchical generalization. Experimental results show that the first method increases accuracy over the baseline by 2.33%, the second one by 0.96% over standard augmentation methods, and IHTA yields an 8.82% and 9.96% higher accuracy than threshold-based and standard augmentation methods, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer models better at understanding common sense ideas. The problem is that these models can be influenced by things like a person’s gender or race, which isn’t fair. To fix this, the researchers propose three new ways to make their model more accurate and fair: changing words that are related to demographics, adding new information to the sentences, and combining both of those approaches. They tested these methods on some examples and found that they made a big difference – the most effective method was able to correctly identify common sense ideas 9% more often than usual. |
Keywords
» Artificial intelligence » Generalization » Natural language processing » Nlp
