enetCollectA New European Network for combining Language Learning with Crowdsourcing Techniques

  1. Lyding, Verena
  2. Nicolas, Lionel
  3. Agerri Gascón, Rodrigo
  4. Maritxalar Anglada, Montserrat
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2018

Issue: 61

Pages: 171-174

Type: Article

More publications in: Procesamiento del lenguaje natural

Abstract

We present enetCollect, a large European COST action network set up with the aim of promoting a research trend combining the well-established domain of Language Learning with recent and successful crowdsourcing approaches. More specifically, the challenge of enetCollect is to foster the language skills of all citizens regardless of their backgrounds by enhancing the production of language learning material using Crowdsourcing techniques. In order to do so, the action will create a balanced interdisciplinary community of active stakeholders related to content-creation, content-usage, and Learning/Content Management Systems to create a theoretical framework for achieving a shared understanding of Language Learning and Crowdsourcing. This will allow to unlock the crowdsourcing potential available for language learning and to facilitate the development of prototypical experiments for the production of language learning material, such as lesson or exercise content. These activities would potentially benefit a wide range of users and languages.

Funding information

The authors have been funded by the Horizon 2020 Framework Programme of the European Union under the enetCollect CA16105 COST action.

Bibliographic References

  • Bos, J., V. Basile, K. Evang, N. Venhuizen, and J. Bjerva. 2017. The groningen meaning bank. In N. Ide and J. Pustejovsky, ed itors, Handbook of Linguistic Annotation, volume 2. Springer, pages 463–496.
  • Guillaume, B., K. Fort, and N. Lefebvre. 2016. Crowdsourcing complex language resources: Playing to annotate dependency syntax. In Proceedings of the International Conference on Computational Linguistics (COLING), Osaka, Japan.
  • Hladká, B., J. Hana, and I. Luksová. 2014. Crowdsourcing in language classes can help natural language processing. In Second AAAI Conference on Human Computation and Crowdsourcing.
  • Lafourcade, M., N. L. Brun, and A. Joubert. 2015. Games with a Purpose (GWAPS). Wiley-ISTWiley-ISTE, July.
  • Poesio, M., J. Chamberlain, U. Kruschwitz, L. Robaldo, and L. Ducceschi. 2012. The phrase detective multilingual corpus, release 0.1. In Collaborative Resource Development and Delivery Workshop Programme, page 34.
  • Sangati, F., S. Merlo, and G. Moretti. 2015. School-tagging: interactive language exercises in classrooms. In LTLT@ SLaTE, pages 16–19.