Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assumethat a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on Learning with Disagreements (Le-wi-Di) wasto provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.

SemEval 2021 Task 12: Learning with Disagreement

Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a novel method to incorporate this disagreement as information: in addition to the standard error computation, we use soft labels (i.e., probability distributions over the annotator labels) as an auxiliary task in a multi-task neural network. We measure the divergence between the predictions and the target soft labels with several loss-functions and evaluate the models on various NLP tasks. We find that the soft-label prediction auxiliary task reduces the penalty for errors on ambiguous entities and thereby mitigates overfitting. It significantly improves performance across tasks beyond the standard approach and prior work.

Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning

SemEval-2021, 15th International Workshop on Semantic Evaluations

workshop paper

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7E-Oral: Machine Learning for NLP: Classification and Structured Prediction Models

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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SemEval 2021 Task 12: Learning with Disagreement

Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning

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