Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer Generation

Vikas Yadav, Hyuk joon Kwon, Vijay Srinivasan, Hongxia Jin

Abstract
Question Answer Generation (QAG) is an effective data augmentation technique to improve the accuracy of question answering systems, especially in low-resource domains. While recent pretrained and large language model-based QAG methods have made substantial progress, they face the critical issue of redundant QA pair generation, affecting downstream QA systems. Implicit diversity techniques such as sampling and diverse beam search are proven effective solutions but often yield smaller diversity. We present explicit diversity conditions for QAG, focusing on spatial aspects, question types, and entities, substantially increasing diversity in QA generation. Our work emphasizes the need of explicit diversity conditions for generating diverse question-answer synthetic data by showing significant improvements in downstream QA task over existing implicit diversity techniques. In particular, generated QA pairs from explicit diversity conditions result in an average 4.1% exact match and 4.5% F1 improvement over implicit sampling techniques on SQuAD-DU. Our work emphasizes the need for explicit diversity conditions even more in low-resource datasets (SubjQA), where average QA performance improvements are ~12% EM.
Anthology ID:
2024.lrec-main.601
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
6876–6882
Language:
URL:
https://aclanthology.org/2024.lrec-main.601
DOI:
Bibkey:
Cite (ACL):
Vikas Yadav, Hyuk joon Kwon, Vijay Srinivasan, and Hongxia Jin. 2024. Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer Generation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6876–6882, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer Generation (Yadav et al., LREC-COLING 2024)
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