Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study
Unlike tasks such as translation or summarization, generating meaningful questions necessitates a profound understanding of context, semantics, and syntax. This complexity arises from the need to not only comprehend the given text comprehensively but also infer information gaps, identify relevant en...
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| Format: | Article |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135334 |
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| author | Julia da Rocha Junqueira Ulisses Brisolara Corrêa Larissa Freitas |
| author_facet | Julia da Rocha Junqueira Ulisses Brisolara Corrêa Larissa Freitas |
| author_sort | Julia da Rocha Junqueira |
| collection | DOAJ |
| description | Unlike tasks such as translation or summarization, generating meaningful questions necessitates a profound understanding of context, semantics, and syntax. This complexity arises from the need to not only comprehend the given text comprehensively but also infer information gaps, identify relevant entities, and construct syntactically and semantically correct interrogative sentences. We address this challenge by proposing an experimental fine-tuning approach for encoder-decoder models (T5, FLAN-T5, and BART-PT) tailored explicitly for Brazilian Portuguese question generation. Our study involves fine-tuning these models on the SQUAD-v1.1 dataset and subsequent evaluation, also on SQUAD-v1.1. Through our experimental endeavors, BART returned a higher result in all the ROUGE metrics, as ROUGE-1 0.46, ROUGE-2 0.24, and ROUGE-L 0.43, suggesting a higher lexical similarity in the questions generated, and it is comparable to the results of the question generation task for the English language. We explored how these advancements can significantly enhance the precision and quality of the question generation task in Brazilian Portuguese, bridging the gap between training data and the intricacies of interrogative sentence construction. |
| format | Article |
| id | doaj-art-5896649fb0274cd7a245feb2d5c4a65f |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-5896649fb0274cd7a245feb2d5c4a65f2025-08-20T03:07:10ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13533471707Transformer Models for Brazilian Portuguese Question Generation: An Experimental StudyJulia da Rocha Junqueira0https://orcid.org/0009-0008-6600-3163Ulisses Brisolara Corrêa1Larissa FreitasUFPELUFPELUnlike tasks such as translation or summarization, generating meaningful questions necessitates a profound understanding of context, semantics, and syntax. This complexity arises from the need to not only comprehend the given text comprehensively but also infer information gaps, identify relevant entities, and construct syntactically and semantically correct interrogative sentences. We address this challenge by proposing an experimental fine-tuning approach for encoder-decoder models (T5, FLAN-T5, and BART-PT) tailored explicitly for Brazilian Portuguese question generation. Our study involves fine-tuning these models on the SQUAD-v1.1 dataset and subsequent evaluation, also on SQUAD-v1.1. Through our experimental endeavors, BART returned a higher result in all the ROUGE metrics, as ROUGE-1 0.46, ROUGE-2 0.24, and ROUGE-L 0.43, suggesting a higher lexical similarity in the questions generated, and it is comparable to the results of the question generation task for the English language. We explored how these advancements can significantly enhance the precision and quality of the question generation task in Brazilian Portuguese, bridging the gap between training data and the intricacies of interrogative sentence construction.https://journals.flvc.org/FLAIRS/article/view/135334natural language processingtransformersparallel multi-head attention mechanismsquestion generationencoder-decoder modelsbrazilian portuguesesquad-v1.1 datasetexperimental fine-tuning |
| spellingShingle | Julia da Rocha Junqueira Ulisses Brisolara Corrêa Larissa Freitas Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study Proceedings of the International Florida Artificial Intelligence Research Society Conference natural language processing transformers parallel multi-head attention mechanisms question generation encoder-decoder models brazilian portuguese squad-v1.1 dataset experimental fine-tuning |
| title | Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study |
| title_full | Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study |
| title_fullStr | Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study |
| title_full_unstemmed | Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study |
| title_short | Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study |
| title_sort | transformer models for brazilian portuguese question generation an experimental study |
| topic | natural language processing transformers parallel multi-head attention mechanisms question generation encoder-decoder models brazilian portuguese squad-v1.1 dataset experimental fine-tuning |
| url | https://journals.flvc.org/FLAIRS/article/view/135334 |
| work_keys_str_mv | AT juliadarochajunqueira transformermodelsforbrazilianportuguesequestiongenerationanexperimentalstudy AT ulissesbrisolaracorrea transformermodelsforbrazilianportuguesequestiongenerationanexperimentalstudy AT larissafreitas transformermodelsforbrazilianportuguesequestiongenerationanexperimentalstudy |