MatRank: Text Re-ranking by Latent Preference Matrix
Jinwen Luo, Jiuding Yang, Weidong Guo, Chenglin Li, Di Niu, Yu Xu · 2022
Text ranking plays a key role in providing content that best answers user queries.It is usually divided into two sub-tasks to perform efficient information retrieval given a query: text retrieval and text re-ranking.Recent research on pretrained language models (PLM) has demonstrated efficiency and gain on both sub-tasks.However, while existing methods have benefited from pre-trained language models and achieved high recall rates on passage retrieval, the ranking performance still demands further improvement.In this paper, we propose Ma-tRank, which learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix.Specifically, MatRank uses a PLM to generate an asymmetric latent matrix of relative preference scores between all pairs of retrieved passages.Then, the latent matrix is aggregated row-wise and column-wise to obtain global preferences and predictions of the most relevant passage in two of these directions, respectively.We conduct extensive experiments on MS MACRO, WikiAQ, and Se-mEval datasets.Experimental results show that MatRank has achieved new state-of-the-art results on these datasets, outperforming all prior methods on ranking performance metrics.