Exploring the Benefits of Data Augmentation in Math Word Problem Solving

Yigit, Gulsum; Amasyali, Mehmet Fatih

doi:10.1109/inista59065.2023.10310417

2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA) 2023

DOI: 10.1109/inista59065.2023.10310417

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Exploring the Benefits of Data Augmentation in Math Word Problem Solving

Gulsum Yigit,

Mehmet Fatih Amasyali

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2024

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Cited by 2 publications

References 27 publications

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From text to multimodal: a survey of adversarial example generation in question answering systems

Yigit,

Amasyali

2024

Knowl Inf Syst

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Integrating adversarial machine learning with question answering (QA) systems has emerged as a critical area for understanding the vulnerabilities and robustness of these systems. This article aims to review adversarial example-generation techniques in the QA field, including textual and multimodal contexts. We examine the techniques employed through systematic categorization, providing a structured review. Beginning with an overview of traditional QA models, we traverse the adversarial example generation by exploring rule-based perturbations and advanced generative models. We then extend our research to include multimodal QA systems, analyze them across various methods, and examine generative models, seq2seq architectures, and hybrid methodologies. Our research grows to different defense strategies, adversarial datasets, and evaluation metrics and illustrates the literature on adversarial QA. Finally, the paper considers the future landscape of adversarial question generation, highlighting potential research directions that can advance textual and multimodal QA systems in the context of adversarial challenges.

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From text to multimodal: a survey of adversarial example generation in question answering systems

Yigit,

Amasyali

2024

Knowl Inf Syst

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Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving

Yigit,

Amasyali

2024

SN COMPUT. SCI.

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Exploring the Benefits of Data Augmentation in Math Word Problem Solving

Cited by 2 publications

References 27 publications

From text to multimodal: a survey of adversarial example generation in question answering systems

From text to multimodal: a survey of adversarial example generation in question answering systems

Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving

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