Summary of Transforming Dutch: Debiasing Dutch Coreference Resolution Systems For Non-binary Pronouns, by Goya Van Boven et al.
Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns
by Goya van Boven, Yupei Du, Dong Nguyen
First submitted to arxiv on: 30 Apr 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 paper investigates a Dutch coreference resolution system’s ability to process gender-neutral pronouns, particularly “hen” and “die”, which were introduced in 2016. English NLP systems struggle to correctly handle gender-neutral pronouns, risking erasure and misgendering of non-binary individuals. The authors compare two debiasing techniques: Counterfactual Data Augmentation (CDA) and delexicalisation. They introduce a novel evaluation metric, the pronoun score, to directly measure correct pronoun processing. Results show diminished performance on gender-neutral pronouns but CDA substantially reduces the gap between gendered and gender-neutral pronouns. The study highlights the effectiveness of debiasing with minimal resources and low computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well a computer program can understand words like “hen” and “die”, which are new ways to refer to people that don’t fit into male or female categories. Right now, English computer programs have trouble understanding these words, which means they might accidentally erase or misrepresent non-binary individuals. The researchers compared two methods for making the program more fair: one uses fake data and the other removes personal details. They also created a new way to measure how well the program is doing. The results show that the program does worse with new words, but one of the methods makes it do much better. This study shows that we can make computer programs understand new words without needing a lot of extra resources or complicated math. |
Keywords
» Artificial intelligence » Coreference » Data augmentation » Nlp
