  Conversational context understanding aims to recognize the real intention of
user from the conversation history, which is critical for building the dialogue
system. However, the multi-turn conversation understanding in open domain is
still quite challenging, which requires the system extracting the important
information and resolving the dependencies in contexts among a variety of open
topics. In this paper, we propose the conversational query rewriting model -
MLR, which is a Multi-task model on sequence Labeling and query Rewriting. MLR
reformulates the multi-turn conversational queries into a single turn query,
which conveys the true intention of users concisely and alleviates the
difficulty of the multi-turn dialogue modeling. In the model, we formulate the
query rewriting as a sequence generation problem and introduce word category
information via the auxiliary word category label predicting task. To train our
model, we construct a new Chinese query rewriting dataset and conduct
experiments on it. The experimental results show that our model outperforms
compared models, and prove the effectiveness of the word category information
in improving the rewriting performance.
