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The Conversational Question Answering (CoQA) task involves answering asequence of inter-related conversational questions about a contextualparagraph. Although existing approaches employ human-written ground-truthanswers for answering conversational questions at test time, in a realisticscenario, the CoQA model will not have any access to ground-truth answers forthe previous questions, compelling the model to rely upon its own previouslypredicted answers for answering the subsequent questions. In this paper, wefind that compounding errors occur when using previously predicted answers attest time, significantly lowering the performance of CoQA systems. To solvethis problem, we propose a sampling strategy that dynamically selects betweentarget answers and model predictions during training, thereby closelysimulating the situation at test time. Further, we analyse the severity of thisphenomena as a function of the question type, conversation length and domaintype.

Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering