A practical toolkit for multilingual question and answer generation

Ushio, Asahi, Alva Manchego, Fernando and Camacho-Collados, Jose 2023. A practical toolkit for multilingual question and answer generation. Presented at: 61st Annual Meeting of the Association for Computational Linguistics, 9-14 July 2023. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: System Demonstrations. , vol.3 Association for Computational Linguistics, pp. 86-94. 10.18653/v1/2023.acl-demo.8

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Abstract

Generating questions along with associated answers from a text has applications in several domains, such as creating reading comprehension tests for students, or improving document search by providing auxiliary questions and answers based on the query. Training models for question and answer generation (QAG) is not straightforward due to the expected structured output (i.e. a list of question and answer pairs), as it requires more than generating a single sentence. This results in a small number of publicly accessible QAG models. In this paper, we introduce AutoQG, an online service for multilingual QAG along with lmqg, an all-in-one python package for model fine-tuning, generation, and evaluation. We also release QAG models in eight languages fine-tuned on a few variants of pre-trained encoder-decoder language models, which can be used online via AutoQG or locally via lmqg. With these resources, practitioners of any level can benefit from a toolkit that includes a web interface for end users, and easy-to-use code for developers who require custom models or fine-grained controls for generation.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Computer Science & Informatics
Publisher:	Association for Computational Linguistics
Last Modified:	12 Jun 2024 12:35
URI:	https://orca.cardiff.ac.uk/id/eprint/161905

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