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Most works on modeling the conversation history in Conversational QuestionAnswering (CQA) report a single main result on a common CQA benchmark. Whileexisting models show impressive results on CQA leaderboards, it remains unclearwhether they are robust to shifts in setting (sometimes to more realisticones), training data size (e.g. from large to small sets) and domain. In thiswork, we design and conduct the first large-scale robustness study of historymodeling approaches for CQA. We find that high benchmark scores do notnecessarily translate to strong robustness, and that various methods canperform extremely differently under different settings. Equipped with theinsights from our study, we design a novel prompt-based history modelingapproach, and demonstrate its strong robustness across various settings. Ourapproach is inspired by existing methods that highlight historic answers in thepassage. However, instead of highlighting by modifying the passage tokenembeddings, we add textual prompts directly in the passage text. Our approachis simple, easy-to-plug into practically any model, and highly effective, thuswe recommend it as a starting point for future model developers. We also hopethat our study and insights will raise awareness to the importance ofrobustness-focused evaluation, in addition to obtaining high leaderboardscores, leading to better CQA systems.

On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method