117 citations · 414 across the 107 of their papers we have counts for
20 papers · 1 filter
What is Event Knowledge Graph: A Survey
Saiping Guan, Xueqi Cheng, Long Bai +5
Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are also an essential kind of knowledge in the world, which trigger the spring up of event-centr…
Interpreting Dense Retrieval as Mixture of Topics
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we…
Pre-training Methods in Information Retrieval
Yixing Fan, Xiaohui Xie, Yinqiong Cai +5
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. In r…
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of stor…
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction
Long Bai, Saiping Guan, Jiafeng Guo +3
Scripts are structured sequences of events together with the participants, which are extracted from the texts.Script event prediction aims to predict the subsequent event given the…
FedMatch: Federated Learning Over Heterogeneous Question Answering Data
Jiangui Chen, Ruqing Zhang, Jiafeng Guo +2
Question Answering (QA), a popular and promising technique for intelligent information access, faces a dilemma about data as most other AI techniques. On one hand, modern QA method…
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict
Yiyi Liu, Yequan Wang, Aixin Sun +3
Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa. The essence of sarcasm, which is also a sufficient and necessary conditi…
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Recently, Information Retrieval community has witnessed fast-paced advances in Dense Retrieval (DR), which performs first-stage retrieval with embedding-based search. Despite the i…
Toward the Understanding of Deep Text Matching Models for Information Retrieval
Lijuan Chen, Yanyan Lan, Liang Pang +2
Semantic text matching is a critical problem in information retrieval. Recently, deep learning techniques have been widely used in this area and obtained significant performance im…
Are Neural Ranking Models Robust?
Chen Wu, Ruqing Zhang, Jiafeng Guo +2
Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking…
A Discriminative Semantic Ranker for Question Retrieval
Yinqiong Cai, Yixing Fan, Jiafeng Guo +3
Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typica…
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Zixuan Li, Xiaolong Jin, Saiping Guan +4
Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenge…
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
Saiping Guan, Xiaolong Jin, Jiafeng Guo +2
With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focu…
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
Zixuan Li, Xiaolong Jin, Wei Li +5
Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future…
Optimizing Dense Retrieval Model Training with Hard Negatives
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Ranking has always been one of the top concerns in information retrieval researches. For decades, the lexical matching signal has dominated the ad-hoc retrieval process, but solely…
Sketch and Customize: A Counterfactual Story Generator
Changying Hao, Liang Pang, Yanyan Lan +3
Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text.…
B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval
Xinyu Ma, Jiafeng Guo, Ruqing Zhang +3
Pre-training and fine-tuning have achieved remarkable success in many downstream natural language processing (NLP) tasks. Recently, pre-training methods tailored for information re…
Semantic Models for the First-stage Retrieval: A Comprehensive Review
Jiafeng Guo, Yinqiong Cai, Yixing Fan +3
Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stag…
A Linguistic Study on Relevance Modeling in Information Retrieval
Yixing Fan, Jiafeng Guo, Xinyu Ma +3
Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has…
Learning to Truncate Ranked Lists for Information Retrieval
Chen Wu, Ruqing Zhang, Jiafeng Guo +3
Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically d…