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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Berger, Gonzalo | - |
dc.contributor.author | Rischewski, Tatiana | - |
dc.contributor.author | Chiruzzo, Luis | - |
dc.contributor.author | Rosá, Aiala | - |
dc.date.accessioned | 2023-05-16T16:27:08Z | - |
dc.date.available | 2023-05-16T16:27:08Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | 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 | es |
dc.identifier.uri | https://hdl.handle.net/20.500.12008/37155 | - |
dc.description | 2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 23-25 November 2022, Montevideo, Uruguay. | es |
dc.description.abstract | 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. | es |
dc.description.sponsorship | Agencia Nacional de Investigación e Innovación. Proyecto FSED_2_2020_1_163587. | es |
dc.format.extent | 5 p. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | en | es |
dc.publisher | IEEE | es |
dc.rights | Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014) | es |
dc.subject | NLP for language teaching | es |
dc.subject | Question & answering | es |
dc.subject | Transformers | es |
dc.subject | Neural language models | es |
dc.title | Generation of english question answer exercises from texts using transformers based models | es |
dc.type | Ponencia | es |
dc.contributor.filiacion | Berger Gonzalo, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
dc.contributor.filiacion | Rischewski Tatiana, Universidad de la República (Uruguay). Facultad de Ingeniería | - |
dc.contributor.filiacion | Chiruzzo Luis, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
dc.contributor.filiacion | Rosá Aiala, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
dc.rights.licence | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
dc.identifier.doi | 10.1109/LA-CCI54402.2022.9981171 | - |
Aparece en las colecciones: | Reportes Técnicos - Instituto de Computación |
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BRCR22.pdf | Ponencia | 178,45 kB | Adobe PDF | Visualizar/Abrir |
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