@inproceedings{45e3ff1b6a9a499a8aca1d8248448d6f,
title = "Data Augmentation for Question Answering Using Transformer-based VAE with Negative Sampling",
abstract = "In this paper, we propose a method to improve the accuracy of extracting appropriate question-answer pairs using generated questions with negative sampling. The base question-answering system that extracts similar questions for input queries is constructed on a Sentence-BERT model to carry out pairwised-ranking between questions of question-answer data and the input queries. The key issue of improving the question answering system is how we can prepare the enough size and variety of training examples. The Sentence-BERT model is trained on positive and negative pairs of extended questions generated by a Transformer-based Variational Autoencoder as well as human. Experimental results show that performance of retrieving appropriate questions for input queries is improved when the Sentence-BERT model is trained with the negative samples that are most similar to the positive examples.",
keywords = "Negative sampling, Question answering system, Sentence-BERT, Variational Autoencoder",
author = "Wataru Kano and Koichi Takeuchi",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022 ; Conference date: 02-07-2022 Through 07-07-2022",
year = "2022",
doi = "10.1109/IIAIAAI55812.2022.00097",
language = "English",
series = "Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "467--470",
editor = "Tokuro Matsuo and Kunihiko Takamatsu and Yuichi Ono",
booktitle = "Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022",
}