Gamification Platform for Collecting Task-oriented Dialogue Data

Haruna Ogawa, Hitoshi Nishikawa, Takenobu Tokunaga, Hikaru Yokono


Abstract
Demand for massive language resources is increasing as the data-driven approach has established a leading position in Natural Language Processing. However, creating dialogue corpora is still a difficult task due to the complexity of the human dialogue structure and the diversity of dialogue topics. Though crowdsourcing is majorly used to assemble such data, it presents problems such as less-motivated workers. We propose a platform for collecting task-oriented situated dialogue data by using gamification. Combining a video game with data collection benefits such as motivating workers and cost reduction. Our platform enables data collectors to create their original video game in which they can collect dialogue data of various types of tasks by using the logging function of the platform. Also, the platform provides the annotation function that enables players to annotate their own utterances. The annotation can be gamified aswell. We aim at high-quality annotation by introducing such self-annotation method. We implemented a prototype of the proposed platform and conducted a preliminary evaluation to obtain promising results in terms of both dialogue data collection and self-annotation.
Anthology ID:
2020.lrec-1.876
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
7084–7093
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.876
DOI:
Bibkey:
Cite (ACL):
Haruna Ogawa, Hitoshi Nishikawa, Takenobu Tokunaga, and Hikaru Yokono. 2020. Gamification Platform for Collecting Task-oriented Dialogue Data. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 7084–7093, Marseille, France. European Language Resources Association.
Cite (Informal):
Gamification Platform for Collecting Task-oriented Dialogue Data (Ogawa et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.876.pdf