MatRank: Text Re-ranking by Latent Preference Matrix
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.
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Text ranking plays a key role in providing con- tent that best answers user queries. It is usually divided into two sub-tasks to perform efficient.
Topic models can be used to improve classification of protein-protein interactions (PPIs) by condensing lexical knowledge available in unannotated biomedical ...
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This paper focuses on BERT-based document reranking and proposes Dynamic Multi-Granularity Learning (DML), a model that significantly outperforms previous ...
MatRank: Text Re-ranking by Latent Preference Matrix. J Luo, J Yang, W Guo, C Li, D Niu, Y Xu. Findings of the Association for Computational Linguistics: EMNLP ...
MatRank: Text Re-ranking by Latent Preference Matrix. record by Chenglin Li • MatRank: Text Re-ranking by Latent Preference Matrix. Jinwen Luo, Jiuding Yang ...
Learning-to-Rank with BERT in TF-Ranking - Semantic Scholar
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Apr 17, 2020 · This paper proposes RankT5 and studies two T5-based ranking model structures that can be fine-tuned with pairwise or listwise ranking losses to optimize ...
Our goal is to find a rank-prediction rule that assigns each instance a rank which is as close as possible to the instance's true rank. We describe a simple and ...