TY - JOUR AU - Uma, Alexandra AU - Fornaciari, Tommaso AU - Hovy, Dirk AU - Paun, Silviu AU - Plank, Barbara AU - Poesio, Massimo PY - 2020/10/01 Y2 - 2024/03/29 TI - A Case for Soft Loss Functions JF - Proceedings of the AAAI Conference on Human Computation and Crowdsourcing JA - HCOMP VL - 8 IS - 1 SE - Short Papers DO - 10.1609/hcomp.v8i1.7478 UR - https://ojs.aaai.org/index.php/HCOMP/article/view/7478 SP - 173-177 AB - <p class="abstract">Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. In this paper, we generalize these results by showing that training with soft labels is an effective method for using crowd annotations in several other ai tasks besides the one studied by Peterson <em>et al.</em>, and also when their performance is compared with that of state-of-the-art methods for learning from crowdsourced data.</p> ER -