  The Chinese academy of sciences Information Retrieval team (CIR) has
participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches
and reports our results on the ULTRE-2 task. We recognize the issue of false
negatives in the Baidu search data in this competition is very severe, much
more 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 study
how to alleviate the false negative issue. We approach the problem from two
perspectives: 1) correcting the labels for non-clicked items by a relevance
judgment model trained from DLA, and learn a new ranker that is initialized
from DLA; 2) including random documents as true negatives and documents that
have partial matching as hard negatives. Both methods can enhance the model
performance and our best method has achieved nDCG@10 of 0.5355, which is 2.66%
better than the best score from the organizer.
