enetCollect: Una nueva red europea para el aprendizaje de idiomas y el crowdsourcing

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.En este artículo presentamos enetCollect, una extensa acción europea COST diseñada con el objetivo de promover una nueva línea de investigación que combine el dominio del aprendizaje de idiomas con recientes y exitosos enfoques basados en crowdsourcing. Más específicamente, el reto de enetCollect es fomentar el aprendizaje de idiomas para toda la ciudadanía europea mediante la mejora en la producción de materiales para el aprendizaje de idiomas usando técnicas de crowdsourcing. Para ello, la acción creará una comunidad interdisciplinar de agentes activos relacionados con la creación, uso y gestión de contenidos para el aprendizaje de idiomas que permita generar un marco teórico común en el cual investigar sobre el uso de crowdsourcing para la generación de contenidos y tecnología relacionada con el aprendizaje de idiomas. La idea es liberar el potencial de usar crowdsourcing para el aprendizaje de idiomas y facilitar el desarrollo de experimentos y prototipos para la generación de materiales de aprendizaje, tales como ejercicios, lecciones, etc. Estas actividades beneficiarán a la gran mayoría de las personas en proceso de aprender un nuevo idioma.The authors have been funded by the Horizon 2020 Framework Programme of the European Union under the enetCollect CA16105 COST action

v-trel: Vocabulary Trainer for Tracing Word Relations : An Implicit Crowdsourcing Approach

In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource. We performed an empirical evaluation of our approach with 60 non-native speakers over two days, which shows that new entries to expand Concept-Net can efficiently be gathered through vocabulary exercises on word relations. We also report on the feedback gathered from the users and an expert from language teaching, and discuss the potential of the vocabulary trainer application from the user and language learner perspective. The feedback suggests that v-trel has educational potential, while in its current state some shortcomings could be identified.Peer reviewe

Designing a Prototype Architecture for Crowdsourcing Language Resources

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Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet

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 the 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.Peer reviewe

The PAIS? Corpus of Italian Web Texts

PAISA\u27 is a Creative Commons licensed, large web corpus of contemporary Italian. We describe the design, harvesting, and processing steps involved in its creation

EnetCollect in Italy

In this paper, we present the enetCollect COST Action, a large network project, which aims at initiating a new Research and Innovation (R&I) trend on combining the well-established domain of language learning with recent and successful crowdsourcing approaches. We introduce its objectives, and describe its organization. We then present the Italian network members and detail their research interests within enetCollect. Finally, we report on its progression so far.In questo articolo presentiamo la COST Action enetCollect, un ampio network il cui scopo è avviare un nuovo filone di Ricerca e Innovazione (R&I) combinando l’ambito consolidato dell’apprendimento delle lingue con i più recenti e riusciti approcci di crowdsourcing. Introduciamo i suoi obiettivi e descriviamo la sua organizzazione. Inoltre, presentiamo i membri italiani ed i loro interessi di ricerca all’interno di enetCollect. Infine, descriviamo lo stato di avanzamento finora raggiunto

Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet

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

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-reviewe