575 citations · 1.6k across the 40 of their papers we have counts for
25 papers · 1 filter
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
Zhenghao Liu, Sen Mei, Chenyan Xiong +5
This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text repres…
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…
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…
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…
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Lee Xiong, Chenyan Xiong, Ye Li +5
Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires…
Proceedings of the KG-BIAS Workshop 2020 at AKBC 2020
Edgar Meij, Tara Safavi, Chenyan Xiong +3
The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediati…
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…
TREC CAsT 2019: The Conversational Assistance Track Overview
Jeffrey Dalton, Chenyan Xiong, Jamie Callan
The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test co…
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…
Generic Intent Representation in Web Search
Hongfei Zhang, Xia Song, Chenyan Xiong +4
This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search. Leveraging large scale user clicks from Bing sea…
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…
An Axiomatic Approach to Regularizing Neural Ranking Models
Corby Rosset, Bhaskar Mitra, Chenyan Xiong +3
Axiomatic information retrieval (IR) seeks a set of principle properties desirable in IR models. These properties when formally expressed provide guidance in the search for better…
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…
Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling
Chenyan Xiong, Zhengzhong Liu, Jamie Callan +1
This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents. KESM repres…
Word-Entity Duet Representations for Document Ranking
Chenyan Xiong, Jamie Callan, Tie-Yan Liu
This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations…