Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations

Abstract

Although discourse parsing is an important and fundamental task in natural language processing, few languages have corpora annotated with discourse relations and if any, they are small in size. Creating a new corpus of discourse relations by hand is costly and time-consuming. To cope with this problem, Kawahara et al. (2014) constructed a Japanese corpus with discourse annotations through crowdsourcing. However, they did not evaluate the quality of the annotation. In this paper, we evaluate the quality of the annotation using expert annotations. We find out that crowdsourcing-based annotation still leaves much room for improvement. Based on the error analysis, we propose improvement techniques based on language tests. We re-annotated the corpus with discourse annotations using the improvement techniques, and achieved approximately 3% improvement in F-measure. We will make re-annotated data publicly available.

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