Browsing by Author "Amasyali,M.F."

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Citation Count: 0
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving
(Springer, 2024) Yiğit, Gülsüm; Amasyali,M.F.
Math Word Problem (MWP) solving presents a challenging task in Natural Language Processing (NLP). This study aims to provide MWP solvers with a more diverse training set, ultimately improving their ability to solve various math problems. We propose several methods for data augmentation by modifying the problem texts and equations, such as synonym replacement, rule-based: question replacement, and rule based: reversing question methodologies over two English MWP datasets. This study extends by introducing a new in-context learning augmentation method, employing the Llama-7b language model. This approach involves instruction-based prompting for rephrasing the math problem texts. Performance evaluations are conducted on 9 baseline models, revealing that augmentation methods outperform baseline models. Moreover, concatenating examples generated by various augmentation methods further improves performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
Citation Count: 1
Exploring the Benefits of Data Augmentation in Math Word Problem Solving
(Institute of Electrical and Electronics Engineers Inc., 2023) Yiğit, Gülsüm; Amasyali,M.F.
Math Word Problem (MWP) is a challenging Natural Language Processing (NLP) task. Existing MWP solvers have shown that current models need to generalize better and obtain higher performances. In this study, we aim to enrich existing MWP datasets with high-quality data, which may improve MWP solvers' performances. We propose several data augmentation methods by applying minor modifications to the problem texts and equations of English MWPs datasets which contain equations with one unknown. Extensive experiments on two MWPs datasets have shown that data created by augmented methods have considerably improved performance. Moreover, further increasing the training samples by combining the samples generated by the proposed augmentation methods provides further performance improvements. © 2023 IEEE.