Prompt Guided Copy Mechanism for Conversational Question Answering
Yong Zhang Zhitao Li Jianzong Wang Yiming Gao Ning Cheng Fengying Yu Jing Xiao
Conversational Question Answering (CQA) is a challenging task that aims to
generate natural answers for conversational flow questions. In this paper, we
propose a pluggable approach for extractive methods that introduces a novel
prompt-guided copy mechanism to improve the fluency and appropriateness of the
extracted answers. Our approach uses prompts to link questions to answers and
employs attention to guide the copy mechanism to verify the naturalness of
extracted answers, making necessary edits to ensure that the answers are fluent
and appropriate. The three prompts, including a question-rationale relationship
prompt, a question description prompt, and a conversation history prompt,
enhance the copy mechanism's performance. Our experiments demonstrate that this
approach effectively promotes the generation of natural answers and achieves
good results in the CoQA challenge.