Fine-Tuning and Evaluation of Question Generation for Slovak Language

Ondrej Megela, Daniel Hládek, Matúš Pleva, Ján Staš, Ming Hsiang Su, Yuan Fu Liao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automatic generation of questions about the given context is useful for the adaptation of question-answering systems or to support education. We trained and evaluated a model that generates a question in the Slovak language. We have designed an automatic metric where an additional question-answering model is used to evaluate the generated questions. We calculated how many questions have confidence greater than the given threshold. For generating questions, we used contexts from the Slovak question-answering dataset. The fine-tuned Slovak T5 model did generate 38% of the questions that the evaluation model could answer with confidence greater than 50%. We cooperated with partners from Taiwan during these experiments in the frame of a bilateral project and we plan to transfer the knowledge to the Chinese language later.

Original languageEnglish
Title of host publicationROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing
EditorsJheng-Long Wu, Ming-Hsiang Su, Hen-Hsen Huang, Yu Tsao, Hou-Chiang Tseng, Chia-Hui Chang, Lung-Hao Lee, Yuan-Fu Liao, Wei-Yun Ma
PublisherThe Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages171-178
Number of pages8
ISBN (Electronic)9789869576963
StatePublished - 2023
Event35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023 - Taipei City, Taiwan
Duration: 20 Oct 202321 Oct 2023

Publication series

NameROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing

Conference

Conference35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023
Country/TerritoryTaiwan
CityTaipei City
Period20/10/2321/10/23

Keywords

  • evaluation
  • natural language generation
  • neural networks
  • question answering
  • question generation

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