146 citations · 276 across the 7 of their papers we have counts for
14 papers
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
Xiaomeng Hu, Shi Yu, Chenyan Xiong +3
Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and st…
More Robust Dense Retrieval with Contrastive Dual Learning
Yizhi Li, Zhenghao Liu, Chenyan Xiong +1
Dense retrieval conducts text retrieval in the embedding space and has shown many advantages compared to sparse retrieval. Existing dense retrievers optimize representations of que…
Few-Shot Conversational Dense Retrieval
Shi Yu, Zhenghao Liu, Chenyan Xiong +2
Dense retrieval (DR) has the potential to resolve the query understanding challenge in conversational search by matching in the learned embedding space. However, this adaptation is…
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Zhenghao Liu, Xiaoyuan Yi, Maosong Sun +2
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious cor…
OpenMatch: An Open Source Library for Neu-IR Research
Zhenghao Liu, Kaitao Zhang, Chenyan Xiong +2
OpenMatch is a Python-based library that serves for Neural Information Retrieval (Neu-IR) research. It provides self-contained neural and traditional IR modules, making it easy to…
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Chenyan Xiong, Zhenghao Liu, Si Sun +7
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks. However, these methods are affected by their the cor…