Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet

Younggyun Hahm, Youngbin Noh, Ji Yoon Han, Tae Hwan Oh, Hyonsu Choe, Hansaem Kim, Key-Sun Choi


Abstract
Using current methods, the construction of multilingual resources in FrameNet is an expensive and complex task. While crowdsourcing is a viable alternative, it is difficult to include non-native English speakers in such efforts as they often have difficulty with English-based FrameNet tools. In this work, we investigated cross-lingual issues in crowdsourcing approaches for multilingual FrameNets, specifically in the context of the newly constructed Korean FrameNet. To accomplish this, we evaluated the effectiveness of various crowdsourcing settings whereby certain types of information are provided to workers, such as English definitions in FrameNet or translated definitions. We then evaluated whether the crowdsourced results accurately captured the meaning of frames both cross-culturally and cross-linguistically, and found that by allowing the crowd workers to make intuitive choices, they achieved a quality comparable to that of trained FrameNet experts (F1 > 0.75). The outcomes of this work are now publicly available as a new release of Korean FrameNet 1.1.
Anthology ID:
2020.lrec-1.30
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:
236–244
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.30
DOI:
Bibkey:
Cite (ACL):
Younggyun Hahm, Youngbin Noh, Ji Yoon Han, Tae Hwan Oh, Hyonsu Choe, Hansaem Kim, and Key-Sun Choi. 2020. Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 236–244, Marseille, France. European Language Resources Association.
Cite (Informal):
Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet (Hahm et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.30.pdf