Year: 2023
Type: conference-paper
Abstract: This paper addresses the limitations of the common data annotation and training methods for objective single-label classification tasks.Typically, in such tasks annotators are only asked to provide a single label for each sample and annotator disagreement is discarded when a final hard label is decided through majority voting.We challenge this traditional approach, acknowledging that determining the appropriate label can be difficult due to the ambiguity and lack of context in the data samples.Rather than discarding the information from such ambiguous annotations, our soft label method makes use of them for training.Our findings indicate that additional annotator information, such as confidence, secondary label and disagreement, can be used to effectively generate soft labels.Training classifiers with these soft labels then leads to improved performance and calibration on the hard label test set.
Authors: Ben Wu, Yue Li, Yida Mu, Carolina Scarton, Kalina Bontcheva, Xingyi Song
Institution: University of Sheffield
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389518984