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Creating expert knowledge by relying on language learners : a generic approach for mass-producing language resources by combining implicit crowdsourcing and language learning
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.
peer-reviewed
Crowdsourcing, Computer-assisted instruction, Information storage and retrieval
Crowdsourcing, Computer-assisted instruction, Information storage and retrieval
- University of Malta Malta
- University of Ljubljana Slovenia
- INESC ID - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, INVESTIGACAO E DESENVOLVIMENTO EM LISBOA Portugal
- Eurac Research Italy
- CLARIN ERIC Netherlands
5516
Biemann, C. (2013). Creating a system for lexical substitutions from scratch using crowdsourcing. Language Resources and Evaluation, 47(1):97-122, Mar.
Blanke, T., Bryant, M., Hedges, M., Aschenbrenner, A., and Priddy, M. (2011). Preparing dariah. In 2011 IEEE Seventh International Conference on eScience, pages 158-165. IEEE. [OpenAIRE]
Borg, C. and Gatt, A. (2014). Crowd-sourcing evaluation of automatically acquired, morphologically related word groupings. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3325-3332, Reykjavik, Iceland, May. European Language Resources Association (ELRA).
Bos, J., Basile, V., Evang, K., Venhuizen, N., and Bjerva, J. (2017). The groningen meaning bank. In Nancy Ide et al., editors, Handbook of Linguistic Annotation, volume 2, pages 463-496. Springer.
Callison-Burch, C. and Dredze, M. (2010). Creating speech and language data with amazon's mechanical turk. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, pages 1-12. Association for Computational Linguistics.
Cardenas, R., Borg, C., and Zeman, D. (2019). CUNIMalta system at SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation. In Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 104-112, Florence, Italy, August. Association for Computational Linguistics. [OpenAIRE]
Chamberlain, J., Poesio, M., and Kruschwitz, U. (2008). Phrase detectives: A web-based collaborative annotation game. In Proceedings of the International Conference on Semantic Systems (I-Semantics 08), pages 42-49.
Chamberlain, J., Fort, K., Kruschwitz, U., Lafourcade, M., and Poesio, M. (2013). Using games to create language resources: Successes and limitations of the approach. In Iryna Gurevych et al., editors, The People's Web Meets NLP, Theory and Applications of Natural Language Processing, pages 3-44. Springer Berlin Heidelberg.
Cholakov, K. and Van Noord, G. (2010). Using unknown word techniques to learn known words. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 902-912.
Constant, M., Eryig˘it, G., Monti, J., Van Der Plas, L., Ramisch, C., Rosner, M., and Todirascu, A. (2017). Multiword expression processing: A survey. Computational Linguistics, 43(4):837-892.
- University of Malta Malta
- University of Ljubljana Slovenia
- INESC ID - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, INVESTIGACAO E DESENVOLVIMENTO EM LISBOA Portugal
- Eurac Research Italy
- CLARIN ERIC Netherlands
- Common Language Resources and Technology Infrastructure, European Research Infrastructure Consortium, Utrecht University Netherlands
- European Research Infrastructure for Language Resources and Technology Netherlands
- Jerusalem College of Technology Israel
- UNIVERZA V LJUBLJANI Slovenia
- Eurac Research, Institute for Applied Linguistics Italy
- National University of Ireland, Galway Ireland
- "Alexandru Ioan Cuza" University of Iași Romania
- Open University of Cyprus Cyprus
- University of Helsinki Finland
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.
peer-reviewed