  The task of dialogue rewriting aims to reconstruct the latest dialogue
utterance by copying the missing content from the dialogue context. Until now,
the existing models for this task suffer from the robustness issue, i.e.,
performances drop dramatically when testing on a different domain. We address
this robustness issue by proposing a novel sequence-tagging-based model so that
the search space is significantly reduced, yet the core of this task is still
well covered. As a common issue of most tagging models for text generation, the
model's outputs may lack fluency. To alleviate this issue, we inject the loss
signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge
improvements of our model over the current state-of-the-art systems on domain
transfer.
