  Ellipsis and co-reference are common and ubiquitous especially in multi-turn
dialogues. In this paper, we treat the resolution of ellipsis and co-reference
in dialogue as a problem of generating omitted or referred expressions from the
dialogue context. We therefore propose a unified end-to-end Generative Ellipsis
and CO-reference Resolution model (GECOR) in the context of dialogue. The model
can generate a new pragmatically complete user utterance by alternating the
generation and copy mode for each user utterance. A multi-task learning
framework is further proposed to integrate the GECOR into an end-to-end
task-oriented dialogue. In order to train both the GECOR and the multi-task
learning framework, we manually construct a new dataset on the basis of the
public dataset CamRest676 with both ellipsis and co-reference annotation. On
this dataset, intrinsic evaluations on the resolution of ellipsis and
co-reference show that the GECOR model significantly outperforms the
sequence-to-sequence (seq2seq) baseline model in terms of EM, BLEU and F1 while
extrinsic evaluations on the downstream dialogue task demonstrate that our
multi-task learning framework with GECOR achieves a higher success rate of task
completion than TSCP, a state-of-the-art end-to-end task-oriented dialogue
model.
