59 citations · 93 across the 6 of their papers we have counts for
8 papers
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of stor…
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Recently, Information Retrieval community has witnessed fast-paced advances in Dense Retrieval (DR), which performs first-stage retrieval with embedding-based search. Despite the i…
Optimizing Dense Retrieval Model Training with Hard Negatives
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Ranking has always been one of the top concerns in information retrieval researches. For decades, the lexical matching signal has dominated the ad-hoc retrieval process, but solely…
THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment
Yunqiu Shao, Bulou Liu, Jiaxin Mao +3
In this paper, we present our methodologies for tackling the challenges of legal case retrieval and entailment in the Competition on Legal Information Extraction / Entailment 2020…
Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +2
Ranking has always been one of the top concerns in information retrieval research. For decades, lexical matching signal has dominated the ad-hoc retrieval process, but it also has…
Neural Logic Reasoning
Shaoyun Shi, Hanxiong Chen, Weizhi Ma +3
Recent years have witnessed the success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patter…