Summary of the paper

Title Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations
Authors Yudai Kishimoto, Shinnosuke Sawada, Yugo Murawaki, Daisuke Kawahara and Sadao Kurohashi
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.
Topics Crowdsourcing, Corpus (Creation, Annotation, Etc.), Discourse Annotation, Representation And Processing
Full paper Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations
Bibtex @InProceedings{KISHIMOTO18.640,
  author = {Yudai Kishimoto and Shinnosuke Sawada and Yugo Murawaki and Daisuke Kawahara and Sadao Kurohashi},
  title = "{Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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