575 citations · 1k across the 19 of their papers we have counts for
38 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…
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
Ji Xin, Chenyan Xiong, Ashwin Srinivasan +3
Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search. This requires strong locality…
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling
Huiyuan Xie, Zhenghao Liu, Chenyan Xiong +2
Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new t…
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
HongChien Yu, Chenyan Xiong, Jamie Callan
Dense retrieval systems conduct first-stage retrieval using embedded representations and simple similarity metrics to match a query to documents. Its effectiveness depends on encod…
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…
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
Yu Wang, Jinchao Li, Tristan Naumann +12
Information overload is a prevalent challenge in many high-value domains. A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hund…