LagunTest: A NLP Based Application to Enhance Reading Comprehension

Kepa Bengoetxea, Itziar Gonzalez-Dios, Amaia Aguirregoitia


Abstract
The ability to read and understand written texts plays an important role in education, above all in the last years of primary education. This is especially pertinent in language immersion educational programmes, where some students have low linguistic competence in the languages of instruction. In this context, adapting the texts to the individual needs of each student requires a considerable effort by education professionals. However, language technologies can facilitate the laborious adaptation of materials in order to enhance reading comprehension. In this paper, we present LagunTest, a NLP based application that takes as input a text in Basque or English, and offers synonyms, definitions, examples of the words in different contexts and presents some linguistic characteristics as well as visualizations. LagunTest is based on reusable and open multilingual and multimodal tools, and it is also distributed with an open license. LagunTest is intended to ease the burden of education professionals in the task of adapting materials, and the output should always be supervised by them.
Anthology ID:
2020.readi-1.10
Volume:
Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | READI | WS
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
63–69
Language:
English
URL:
https://aclanthology.org/2020.readi-1.10
DOI:
Bibkey:
Cite (ACL):
Kepa Bengoetxea, Itziar Gonzalez-Dios, and Amaia Aguirregoitia. 2020. LagunTest: A NLP Based Application to Enhance Reading Comprehension. In Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI), pages 63–69, Marseille, France. European Language Resources Association.
Cite (Informal):
LagunTest: A NLP Based Application to Enhance Reading Comprehension (Bengoetxea et al., READI 2020)
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PDF:
https://aclanthology.org/2020.readi-1.10.pdf