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Past works that investigate out-of-domain performance of QA systems havemainly focused on general domains (e.g. news domain, wikipedia domain),underestimating the importance of subdomains defined by the internalcharacteristics of QA datasets. In this paper, we extend the scope of"out-of-domain" by splitting QA examples into different subdomains according totheir several internal characteristics including question type, text length,answer position. We then examine the performance of QA systems trained on thedata from different subdomains. Experimental results show that the performanceof QA systems can be significantly reduced when the train data and test datacome from different subdomains. These results question the generalizability ofcurrent QA systems in multiple subdomains, suggesting the need to combat thebias introduced by the internal characteristics of QA datasets.

Extending the Scope of Out-of-Domain: Examining QA models in multiple subdomains