1.4k citations · 5.4k across the 120 of their papers we have counts for
5 papers · 2 filters
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
Si Sun, Yingzhuo Qian, Zhenghao Liu +5
The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world rank…
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…
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
Zheni Zeng, Chaojun Xiao, Yuan Yao +5
Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (e.g., cold start) in real-world scenarios. Recently pre-trained…
Few-Shot Generative Conversational Query Rewriting
Shi Yu, Jiahua Liu, Jingqin Yang +4
Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing inform…
Selective Weak Supervision for Neural Information Retrieval
Kaitao Zhang, Chenyan Xiong, Zhenghao Liu +1
This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available. We revisit the classic IR intuition that anchor-do…