Creating expert knowledge by relying on language learners: a generic approach for mass-producing language resources by combining implicit crowdsourcing and language learning
Journal
Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)
Date Issued
2020
Author(s)
Nicolas, Lionel
Lyding, Verena
Borg, Claudia
Forascu, Corina
Fort, Karën
Kosem, Iztok
Čibej, Jaka
Arhar Holdt, Š
Millour, Alice
König, Alexander
Rodosthenous, Christos
Sangati, Federico
ul Hassan, Umair
Katinskaia, Anisia
Barreiro, Anabela
Aparaschivei, Lavinia
HaCohen-Kerner, Yaakov
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.
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.
Subjects
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