1.4k citations · 5.4k across the 120 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
Explore Entity Embedding Effectiveness in Entity Retrieval
Zhenghao Liu, Chenyan Xiong, Maosong Sun +1
This paper explores entity embedding effectiveness in ad-hoc entity retrieval, which introduces distributed representation of entities into entity retrieval. The knowledge graph co…
Neural Document Expansion with User Feedback
Yue Yin, Chenyan Xiong, Cheng Luo +1
This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models. NeuDEF harvests expansion terms from queries whi…
Understanding the Behaviors of BERT in Ranking
Yifan Qiao, Chenyan Xiong, Zhenghao Liu +1
This paper studies the performances and behaviors of BERT in ranking tasks. We explore several different ways to leverage the pre-trained BERT and fine-tune it on two ranking tasks…
Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Chao Li, Zhiyuan Liu, Mengmeng Wu +7
Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items. The matching stage retrieves ca…
Consistency and Variation in Kernel Neural Ranking Model
Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi +4
This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deplo…
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
Zhenghao Liu, Chenyan Xiong, Maosong Sun +1
This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words an…
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
Chenyan Xiong, Zhuyun Dai, Jamie Callan +2
This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similariti…