  Conversation question answering requires the ability to interpret a question
correctly. Current models, however, are still unsatisfactory due to the
difficulty of understanding the co-references and ellipsis in daily
conversation. Even though generative approaches achieved remarkable progress,
they are still trapped by semantic incompleteness. This paper presents an
action-based approach to recover the complete expression of the question.
Specifically, we first locate the positions of co-reference or ellipsis in the
question while assigning the corresponding action to each candidate span. We
then look for matching phrases related to the candidate clues in the
conversation context. Finally, according to the predicted action, we decide
whether to replace the co-reference or supplement the ellipsis with the matched
information. We demonstrate the effectiveness of our method on both English and
Chinese utterance rewrite tasks, improving the state-of-the-art EM (exact
match) by 3.9\% and ROUGE-L by 1.0\% respectively on the Restoration-200K
dataset.
