Título:
Generation of english question answer exercises from texts using transformers based models
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Autor:
Berger, Gonzalo
Rischewski, Tatiana
Chiruzzo, Luis
Rosá, Aiala
Rischewski, Tatiana
Chiruzzo, Luis
Rosá, Aiala
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Tipo de documento:
Ponencia
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Palabras clave:
NLP for language teaching
Question & answering
Transformers
Neural language models
Question & answering
Transformers
Neural language models
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Año de publicación:
2022
Contenido:
Resumen:
This paper studies the use of NLP techniques, in particular, neural language models, for the generation of
question/answer exercises from English texts. The experiments aim to generate beginner-level exercises from simple texts, to be used in teaching ESL (English as a Second Language) to children.
The approach we present in this paper is based on four stages: a pre-processing stage that, among other basic tasks, applies a co-reference resolution tool; an answer candidate selection stage, which is based on semantic role labeling; a question generation stage, which takes as input the text with the resolved co-references
and returns a set of questions for each answer candidate using a language model based on the Transformers architecture; and a post-processing stage that adjusts the format of the generated questions. The question generation model was evaluated on a benchmark obtaining similar results to those of previous works,
and the complete pipeline was evaluated on a corpus specifically created for this task, achieving good results.
Descripción:
2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 23-25 November 2022, Montevideo, Uruguay.
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IEEE
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Financiadores:
Agencia Nacional de Investigación e Innovación. Proyecto FSED_2_2020_1_163587.
Citación:
Berger, G., Rischewski, T., Chiruzzo, L. y otros. Generation of english question answer exercises from texts using transformers based models [en línea] EN : 2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 23-25 November 2022, Montevideo, Uruguay. 5 p. DOI: 10.1109/LA-CCI54402.2022.9981171
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| BRCR22.pdf | Ponencia | 178.45 KB | Adobe PDF |