Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet

Christos Rodosthenous, Verena Lyding, Federico Sangati, Alexander König, Umair Ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, Lavinia Aparaschivei

Research output: Conference materialsPaper


In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet. V-TREL is built on top of a generic architecture implementing the implicit crowdsourding paradigm in order to offer vocabulary training exercises generated from the commonsense knowledge-base ConceptNet and -- in the background -- to collect and evaluate the learners' answers to extend ConceptNet with new words. In the experiment about 90 university students learning English at C1 level, based on Common European Framework of Reference for Languages (CEFR), trained their vocabulary with V-TREL over a period of 16 calendar days. The experiment allowed to gather more than 12,000 answers from learners on different question types. In this paper we present in detail the experimental setup and the outcome of the experiment, which indicates the potential of our approach for both crowdsourcing data as well as fostering vocabulary skills.
Original languageEnglish
Number of pages316
Publication statusPublished - 2020
MoE publication typeNot Eligible
EventThe 12th Language Resources and Evaluation Conference - Le Palais du Pharo, Marseille, France
Duration: 11 May 202016 May 2020
Conference number: 12


ConferenceThe 12th Language Resources and Evaluation Conference
Abbreviated titleLREC 2020
Internet address

Cite this

Rodosthenous, C., Lyding, V., Sangati, F., König, A., Ul Hassan, U., Nicolas, L., ... Aparaschivei, L. (2020). Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet. 307. Paper presented at The 12th Language Resources and Evaluation Conference, Marseille, France.