2023/04/03 by Valentina Pyatkin, Pyatkin, Valentina, Frances Yung +9 · 2 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Mobile Crowdsensing and Crowdsourcing #Multi-Agent Systems and Negotiation #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2304.00815
openalex publication_date 2023/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Disagreement in natural language annotation has mostly been studied from a perspective of biases introduced by the annotators and the annotation frameworks. Here, we propose to analyze another source of bias: task design bias, which has a particularly strong impact on crowdsourced linguistic annotations where natural language is used to elicit the interpretation of laymen annotators. For this purpose we look at implicit discourse relation annotation, a task that has repeatedly been shown to be difficult due to the relations' ambiguity. We compare the annotations of 1,200 discourse relations obtained using two distinct annotation tasks and quantify the biases of both methods across four different domains. Both methods are natural language annotation tasks designed for crowdsourcing. We show that the task design can push annotators towards certain relations and that some discourse relations senses can be better elicited with one or the other annotation approach. We also conclude that this type of bias should be taken into account when training and testing models.