  Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161
Session Search (SS) Task. This paper describes our approaches and results. In
the FOSS subtask, we submit five runs using learning-to-rank and fine-tuned
pre-trained language models. We fine-tuned the pre-trained language model with
ad-hoc data and session information and assembled them by a learning-to-rank
method. The assembled model achieves the best performance among all
participants in the preliminary evaluation. In the POSS subtask, we used an
assembled model which also achieves the best performance in the preliminary
evaluation.
