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On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method. (arXiv:2206.14796v1 [cs.CL])
June 30, 2022, 1:12 a.m. | Zorik Gekhman, Nadav Oved, Orgad Keller, Idan Szpektor, Roi Reichart
cs.CL updates on arXiv.org arxiv.org
Most works on modeling the conversation history in Conversational Question
Answering (CQA) report a single main result on a common CQA benchmark. While
existing models show impressive results on CQA leaderboards, it remains unclear
whether they are robust to shifts in setting (sometimes to more realistic
ones), training data size (e.g. from large to small sets) and domain. In this
work, we design and conduct the first large-scale robustness study of history
modeling approaches for CQA. We find that high …
arxiv conversational history question answering representation robustness study
More from arxiv.org / cs.CL updates on arXiv.org
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