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The Chinese academy of sciences Information Retrieval team (CIR) hasparticipated in the NTCIR-17 ULTRE-2 task. This paper describes our approachesand reports our results on the ULTRE-2 task. We recognize the issue of falsenegatives in the Baidu search data in this competition is very severe, muchmore severe than position bias. Hence, we adopt the Dual Learning Algorithm(DLA) to address the position bias and use it as an auxiliary model to studyhow to alleviate the false negative issue. We approach the problem from twoperspectives: 1) correcting the labels for non-clicked items by a relevancejudgment model trained from DLA, and learn a new ranker that is initializedfrom DLA; 2) including random documents as true negatives and documents thathave partial matching as hard negatives. Both methods can enhance the modelperformance and our best method has achieved nDCG@10 of 0.5355, which is 2.66%better than the best score from the organizer.

CIR at the NTCIR-17 ULTRE-2 Task