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Title: Creating expert knowledge by relying on language learners : a generic approach for mass-producing language resources by combining implicit crowdsourcing and language learning
Authors: Nicholas, Lionel
Lyding, Verena
Borg, Claudia
Forascu, Corina
Fort, Karen
Zdravkova, Katerina
Kosem, Iztok
Cibej, Jaka
Holdt, Spela Arhar
Millour, Alice
Konig, Alexander
Rodosthenous, Christos
Sangati, Federico
Hassan, Umair ul
Katinskaia, Anisia
Barreiro, Anabela
Aparaschivei, Lavina
HaCohen-Kerner, Yaakov
Keywords: Crowdsourcing
Computer-assisted instruction
Information storage and retrieval
Issue Date: 2020-05
Citation: Nicolas, L., Lyding, V., Borg, C., Forăscu, C., Fort, K., Zdravkova, K., ... & HaCohen-Kerner, Y. (2020, May). Creating expert knowledge by relying on language learners: a generic approach for mass-producing language resources by combining implicit crowdsourcing and language learning. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 268-278).
Abstract: We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of the generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs.
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