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In light of the success of the pre-trained language models (PLMs), continualpre-training of generic PLMs has been the paradigm of domain adaption. In thispaper, we propose QUERT, A Continual Pre-trained Language Model for QUERyUnderstanding in Travel Domain Search. QUERT is jointly trained on fourtailored pre-training tasks to the characteristics of query in travel domainsearch: Geography-aware Mask Prediction, Geohash Code Prediction, User ClickBehavior Learning, and Phrase and Token Order Prediction. Performanceimprovement of downstream tasks and ablation experiment demonstrate theeffectiveness of our proposed pre-training tasks. To be specific, the averageperformance of downstream tasks increases by 2.02% and 30.93% in supervised andunsupervised settings, respectively. To check on the improvement of QUERT toonline business, we deploy QUERT and perform A/B testing on Fliggy APP. Thefeedback results show that QUERT increases the Unique Click-Through Rate andPage 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.

QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search