@inproceedings{rodosthenous-etal-2020-using,
title = "Using Crowdsourced Exercises for Vocabulary Training to Expand {C}oncept{N}et",
author = {Rodosthenous, Christos and
Lyding, Verena and
Sangati, Federico and
K{\"o}nig, Alexander and
ul Hassan, Umair and
Nicolas, Lionel and
Horbacauskiene, Jolita and
Katinskaia, Anisia and
Aparaschivei, Lavinia},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.38",
pages = "307--316",
abstract = "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.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet
%A Rodosthenous, Christos
%A Lyding, Verena
%A Sangati, Federico
%A König, Alexander
%A ul Hassan, Umair
%A Nicolas, Lionel
%A Horbacauskiene, Jolita
%A Katinskaia, Anisia
%A Aparaschivei, Lavinia
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F rodosthenous-etal-2020-using
%X 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.
%U https://aclanthology.org/2020.lrec-1.38
%P 307-316
Markdown (Informal)
[Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet](https://aclanthology.org/2020.lrec-1.38) (Rodosthenous et al., LREC 2020)
ACL
- Christos Rodosthenous, Verena Lyding, Federico Sangati, Alexander König, Umair ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, and Lavinia Aparaschivei. 2020. Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 307–316, Marseille, France. European Language Resources Association.