  Anaphora and ellipses are two common phenomena in dialogues. Without
resolving referring expressions and information omission, dialogue systems may
fail to generate consistent and coherent responses. Traditionally, anaphora is
resolved by coreference resolution and ellipses by query rewrite. In this work,
we propose a novel joint learning framework of modeling coreference resolution
and query rewriting for complex, multi-turn dialogue understanding. Given an
ongoing dialogue between a user and a dialogue assistant, for the user query,
our joint learning model first predicts coreference links between the query and
the dialogue context, and then generates a self-contained rewritten user query.
To evaluate our model, we annotate a dialogue based coreference resolution
dataset, MuDoCo, with rewritten queries. Results show that the performance of
query rewrite can be substantially boosted (+2.3% F1) with the aid of
coreference modeling. Furthermore, our joint model outperforms the
state-of-the-art coreference resolution model (+2% F1) on this dataset.
