  In light of the success of the pre-trained language models (PLMs), continual
pre-training of generic PLMs has been the paradigm of domain adaption. In this
paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy
Understanding in Travel Domain Search. QUERT is jointly trained on four
tailored pre-training tasks to the characteristics of query in travel domain
search: Geography-aware Mask Prediction, Geohash Code Prediction, User Click
Behavior Learning, and Phrase and Token Order Prediction. Performance
improvement of downstream tasks and ablation experiment demonstrate the
effectiveness of our proposed pre-training tasks. To be specific, the average
performance of downstream tasks increases by 2.02% and 30.93% in supervised and
unsupervised settings, respectively. To check on the improvement of QUERT to
online business, we deploy QUERT and perform A/B testing on Fliggy APP. The
feedback results show that QUERT increases the Unique Click-Through Rate and
Page Click-Through Rate by 0.89% and 1.03% when applying QUERT as the encoder.
Our code and downstream task data will be released for future research.
