Appeared in: Proceedings of the 31st Text REtrieval Conference (TREC 2022)
Abstract:
This paper describes our participation in the TREC 2022 Deep Learning Track. In our participation, we applied the Adaptive ReRanking technique on the constructed corpus graph from various first-stage retrieval models, namely the BM25 and SPLADE retrieval models, before applying the reranker, namely the ELECTRA reranking model. In addition, we employed the ColBERT-PRF technique on various first stage retrieval models. Finally, we experimented with ensemble retrieval for implementing both the Adaptive ReRanking and the ColBERT-PRF techniques. We submitted 14 passage ranking runs (including six baseline runs). Among the submitted runs, the run where the Adaptive ReRanking technique is applied on the ensemble of BM25 and SPLADE retrieval, namely uogtr_e_gb, is the most effective in terms of nDCG@10.
BibTeX @inproceedings{wang:trec2022-dl, author = {Wang, Xiao and MacAvaney, Sean and Macdonald, Craig and Ounis, Iadh}, title = {Experiments with Adaptive ReRanking and ColBERT-PRF: University of Glasgow Terrier Team at TREC DL 2022}, booktitle = {Proceedings of the 31st Text REtrieval Conference}, year = {2022} }