Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition

• URL: http://arxiv.org/abs/2105.14980v1
• Date: Mon, 31 May 2021 14:11:08 GMT
• Title: Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
• Authors: Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Pengjun Xie
• Abstract summary: We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. We find that crowdsourcing could be highly similar to domain adaptation, and then the recent advances of cross-domain methods can be almost directly applied to crowdsourcing. We investigate both unsupervised and supervised crowdsourcing learning, assuming that no or only small-scale expert annotations are available.
• Score: 19.379850806513232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. In this way, we find that crowdsourcing could be highly similar to domain adaptation, and then the recent advances of cross-domain methods can be almost directly applied to crowdsourcing. Here we take named entity recognition (NER) as a study case, suggesting an annotator-aware representation learning model that inspired by the domain adaptation methods which attempt to capture effective domain-aware features. We investigate both unsupervised and supervised crowdsourcing learning, assuming that no or only small-scale expert annotations are available. Experimental results on a benchmark crowdsourced NER dataset show that our method is highly effective, leading to a new state-of-the-art performance. In addition, under the supervised setting, we can achieve impressive performance gains with only a very small scale of expert annotations.

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