Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering

Question Answering (QA) is a natural language processing task that enables the machine to understand a given context and answer a given question. There are several QA research trials containing high resources of the English language. However, Thai is one of the languages that have low availability o...

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Autores principales:	Puri Phakmongkol, Peerapon Vateekul
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	natural language processing question answering machine reading comprehension Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ecf1cd17b35a4ae1b396740540879c07
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id	oai:doaj.org-article:ecf1cd17b35a4ae1b396740540879c07
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spelling	oai:doaj.org-article:ecf1cd17b35a4ae1b396740540879c072021-11-11T15:18:11ZEnhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering10.3390/app1121102672076-3417https://doaj.org/article/ecf1cd17b35a4ae1b396740540879c072021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10267https://doaj.org/toc/2076-3417Question Answering (QA) is a natural language processing task that enables the machine to understand a given context and answer a given question. There are several QA research trials containing high resources of the English language. However, Thai is one of the languages that have low availability of labeled corpora in QA studies. According to previous studies, while the English QA models could achieve more than 90% of F1 scores, Thai QA models could obtain only 70% in our baseline. In this study, we aim to improve the performance of Thai QA models by generating more question-answer pairs with Multilingual Text-to-Text Transfer Transformer (mT5) along with data preprocessing methods for Thai. With this method, the question-answer pairs can synthesize more than 100 thousand pairs from provided Thai Wikipedia articles. Utilizing our synthesized data, many fine-tuning strategies were investigated to achieve the highest model performance. Furthermore, we have presented that the syllable-level F1 is a more suitable evaluation measure than Exact Match (EM) and the word-level F1 for Thai QA corpora. The experiment was conducted on two Thai QA corpora: Thai Wiki QA and iApp Wiki QA. The results show that our augmented model is the winner on both datasets compared to other modern transformer models: Roberta and mT5.Puri PhakmongkolPeerapon VateekulMDPI AGarticlenatural language processingquestion answeringmachine reading comprehensionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10267, p 10267 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	natural language processing question answering machine reading comprehension Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	natural language processing question answering machine reading comprehension Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Puri Phakmongkol Peerapon Vateekul Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
description	Question Answering (QA) is a natural language processing task that enables the machine to understand a given context and answer a given question. There are several QA research trials containing high resources of the English language. However, Thai is one of the languages that have low availability of labeled corpora in QA studies. According to previous studies, while the English QA models could achieve more than 90% of F1 scores, Thai QA models could obtain only 70% in our baseline. In this study, we aim to improve the performance of Thai QA models by generating more question-answer pairs with Multilingual Text-to-Text Transfer Transformer (mT5) along with data preprocessing methods for Thai. With this method, the question-answer pairs can synthesize more than 100 thousand pairs from provided Thai Wikipedia articles. Utilizing our synthesized data, many fine-tuning strategies were investigated to achieve the highest model performance. Furthermore, we have presented that the syllable-level F1 is a more suitable evaluation measure than Exact Match (EM) and the word-level F1 for Thai QA corpora. The experiment was conducted on two Thai QA corpora: Thai Wiki QA and iApp Wiki QA. The results show that our augmented model is the winner on both datasets compared to other modern transformer models: Roberta and mT5.
format	article
author	Puri Phakmongkol Peerapon Vateekul
author_facet	Puri Phakmongkol Peerapon Vateekul
author_sort	Puri Phakmongkol
title	Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
title_short	Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
title_full	Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
title_fullStr	Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
title_full_unstemmed	Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
title_sort	enhance text-to-text transfer transformer with generated questions for thai question answering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ecf1cd17b35a4ae1b396740540879c07
work_keys_str_mv	AT puriphakmongkol enhancetexttotexttransfertransformerwithgeneratedquestionsforthaiquestionanswering AT peeraponvateekul enhancetexttotexttransfertransformerwithgeneratedquestionsforthaiquestionanswering
_version_	1718435596610830337

Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering

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