@inproceedings{yadav-etal-2024-explicit,
    title = "Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer Generation",
    author = "Yadav, Vikas  and
      Kwon, Hyuk joon  and
      Srinivasan, Vijay  and
      Jin, Hongxia",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.601",
    pages = "6876--6882",
    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 {\textasciitilde}12{\%} EM.",
}

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 {\textasciitilde}12{\%} EM.
Yadav, Vikas Kwon, Hyuk joon Srinivasan, Vijay Jin, Hongxia
Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer Generation