@inproceedings{lyu-etal-2022-extending,
    title = "Extending the Scope of Out-of-Domain: Examining {QA} models in multiple subdomains",
    author = "Lyu, Chenyang  and
      Foster, Jennifer  and
      Graham, Yvette",
    booktitle = "Proceedings of the Third Workshop on Insights from Negative Results in NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.insights-1.4",
    doi = "10.18653/v1/2022.insights-1.4",
    pages = "24--37",
    abstract = "Past work that investigates out-of-domain performance of QA systems has mainly focused on general domains (e.g. news domain, wikipedia domain), underestimating the importance of subdomains defined by the internal characteristics of QA datasets.In this paper, we extend the scope of {``}out-of-domain{''} by splitting QA examples into different subdomains according to their internal characteristics including question type, text length, answer position. We then examine the performance of QA systems trained on the data from different subdomains. Experimental results show that the performance of QA systems can be significantly reduced when the train data and test data come from different subdomains. These results question the generalizability of current QA systems in multiple subdomains, suggesting the need to combat the bias introduced by the internal characteristics of QA datasets.",
}