Researchers have developed AXLE, a novel pre-annotation method designed to activate annotators' lived experiences during data labeling. This approach uses a scenario vignette and a guided conversation to create a first-person micro-narrative, which is then presented to the annotator before they begin labeling. A study on hate speech detection indicated that AXLE increased identity resonance and sensitivity to harm, while also raising label entropy and decreasing inter-annotator agreement, suggesting it surfaces diverse perspectives rather than enforcing consensus. AI
IMPACT This method could improve the quality and nuance of data annotation for subjective NLP tasks by better incorporating annotator perspectives.
RANK_REASON The cluster contains a research paper detailing a new methodology for data annotation. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- AXLE
- CatalyzeX
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Litmaps
- ScienceCast
- Scite
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