Search Results for author: Jiafeng Guo

Found 71 papers, 37 papers with code

Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

1 code implementation25 Apr 2022 Jingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation.

Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction

1 code implementation22 Apr 2022 Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Xueqi Cheng

% Therefore, in this work, we propose to drop out the decoder and introduce a novel contrastive span prediction task to pre-train the encoder alone.

Contrastive Learning Information Retrieval +1

GERE: Generative Evidence Retrieval for Fact Verification

1 code implementation12 Apr 2022 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng

This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead; ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking; iii) a fixed number of sentences are selected to form the final evidence set.

Claim Verification Fact Verification

PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models

no code implementations4 Apr 2022 Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng

We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list.

Document Ranking Information Retrieval

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

1 code implementation ACL 2022 Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng

Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on.

A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty

no code implementations CVPR 2022 Sihao Yu, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Zizhen Wang, Xueqi Cheng

By reducing the weights of the majority classes, such instances would become more difficult to learn and hurt the overall performance consequently.

imbalanced classification

What is Event Knowledge Graph: A Survey

1 code implementation31 Dec 2021 Saiping Guan, Xueqi Cheng, Long Bai, Fujun Zhang, Zixuan Li, Yutao Zeng, Xiaolong Jin, Jiafeng Guo

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-centric knowledge representation form like Event KG (EKG).

Question Answering Text Generation

Pre-training Methods in Information Retrieval

no code implementations27 Nov 2021 Yixing Fan, Xiaohui Xie, Yinqiong Cai, Jia Chen, Xinyu Ma, Xiangsheng Li, Ruqing Zhang, Jiafeng Guo

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.

Information Retrieval Re-Ranking

Interpreting Dense Retrieval as Mixture of Topics

no code implementations27 Nov 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

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.

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

4 code implementations12 Oct 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.

Information Retrieval Open-Domain Question Answering +1

Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning

no code implementations29 Sep 2021 Jingwei Liu, Yi Gu, Shentong Mo, Zhun Sun, Shumin Han, Jiafeng Guo, Xueqi Cheng

In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information.

Data Augmentation Self-Supervised Learning

Integrating Deep Event-Level and Script-Level Information for Script Event Prediction

1 code implementation EMNLP 2021 Long Bai, Saiping Guan, Jiafeng Guo, Zixuan Li, Xiaolong Jin, Xueqi Cheng

In this paper, we propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction.

Toward the Understanding of Deep Text Matching Models for Information Retrieval

no code implementations16 Aug 2021 Lijuan Chen, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

We further extend these constraints to the semantic settings, which are shown to be better satisfied for all the deep text matching models.

Information Retrieval Semantic Text Matching +1

FedMatch: Federated Learning Over Heterogeneous Question Answering Data

1 code implementation11 Aug 2021 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng

A possible solution to this dilemma is a new approach known as federated learning, which is a privacy-preserving machine learning technique over distributed datasets.

Federated Learning Privacy Preserving +1

Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance

5 code implementations2 Aug 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size.

Information Retrieval Quantization

A Discriminative Semantic Ranker for Question Retrieval

no code implementations18 Jul 2021 Yinqiong Cai, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Yanyan Lan, Xueqi Cheng

However, these methods often lose the discriminative power as term-based methods, thus introduce noise during retrieval and hurt the recall performance.

Question Answering Re-Ranking

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

no code implementations ACL 2021 Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng

Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts.

Knowledge Graphs reinforcement-learning

Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

1 code implementation21 Apr 2021 Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, HuaWei Shen, Yuanzhuo Wang, Xueqi Cheng

To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently.

Representation Learning

Link Prediction on N-ary Relational Data Based on Relatedness Evaluation

1 code implementation21 Apr 2021 Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng

However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity).

Knowledge Graphs Link Prediction

B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval

1 code implementation20 Apr 2021 Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Yingyan Li, Xueqi Cheng

The basic idea of PROP is to construct the \textit{representative words prediction} (ROP) task for pre-training inspired by the query likelihood model.

Information Retrieval Language Modelling +1

Optimizing Dense Retrieval Model Training with Hard Negatives

4 code implementations16 Apr 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance.

Information Retrieval Representation Learning

Sketch and Customize: A Counterfactual Story Generator

1 code implementation2 Apr 2021 Changying Hao, Liang Pang, Yanyan Lan, Yan Wang, Jiafeng Guo, Xueqi Cheng

In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending.

Text Generation

Semantic Models for the First-stage Retrieval: A Comprehensive Review

1 code implementation8 Mar 2021 Jiafeng Guo, Yinqiong Cai, Yixing Fan, Fei Sun, Ruqing Zhang, Xueqi Cheng

We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development.

Re-Ranking Semantic Retrieval

A Linguistic Study on Relevance Modeling in Information Retrieval

no code implementations1 Mar 2021 Yixing Fan, Jiafeng Guo, Xinyu Ma, Ruqing Zhang, Yanyan Lan, Xueqi Cheng

We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question.

Information Retrieval Natural Language Understanding +1

Learning to Truncate Ranked Lists for Information Retrieval

no code implementations25 Feb 2021 Chen Wu, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng

One is the widely adopted metric such as F1 which acts as a balanced objective, and the other is the best F1 under some minimal recall constraint which represents a typical objective in professional search.

Information Retrieval

Dynamic-K Recommendation with Personalized Decision Boundary

no code implementations25 Dec 2020 Yan Gao, Jiafeng Guo, Yanyan Lan, Huaming Liao

The ranking objective is the same as existing methods, i. e., to create a ranking list of items according to users' interests.

Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings

no code implementations COLING 2020 Yutao Zeng, Xiaolong Jin, Saiping Guan, Jiafeng Guo, Xueqi Cheng

To resolve event coreference, existing methods usually calculate the similarities between event mentions and between specific kinds of event arguments.

Coreference Resolution Event Coreference Resolution

Transformation Driven Visual Reasoning

1 code implementation CVPR 2021 Xin Hong, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i. e.~Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views).

Visual Question Answering Visual Reasoning

PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval

1 code implementation20 Oct 2020 Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Xiang Ji, Xueqi Cheng

Recently pre-trained language representation models such as BERT have shown great success when fine-tuned on downstream tasks including information retrieval (IR).

Information Retrieval Language Modelling

Beyond Language: Learning Commonsense from Images for Reasoning

1 code implementation Findings of the Association for Computational Linguistics 2020 Wanqing Cui, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

This paper proposes a novel approach to learn commonsense from images, instead of limited raw texts or costly constructed knowledge bases, for the commonsense reasoning problem in NLP.

Language Modelling Question Answering

Query Understanding via Intent Description Generation

1 code implementation25 Aug 2020 Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query.

Information Retrieval

Continual Domain Adaptation for Machine Reading Comprehension

no code implementations25 Aug 2020 Lixin Su, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

To tackle such a challenge, in this work, we introduce the \textit{Continual Domain Adaptation} (CDA) task for MRC.

Continual Learning Domain Adaptation +3

Ranking Enhanced Dialogue Generation

no code implementations13 Aug 2020 Changying Hao, Liang Pang, Yanyan Lan, Fei Sun, Jiafeng Guo, Xue-Qi Cheng

To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper.

Dialogue Generation Response Generation

On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation

no code implementations ICML 2020 Jianing Li, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng

We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution.

Text Generation

NeuInfer: Knowledge Inference on N-ary Facts

no code implementations ACL 2020 Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xue-Qi Cheng

It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s).

Match$^2$: A Matching over Matching Model for Similar Question Identification

no code implementations21 Jun 2020 Zizhen Wang, Yixing Fan, Jiafeng Guo, Liu Yang, Ruqing Zhang, Yanyan Lan, Xue-Qi Cheng, Hui Jiang, Xiaozhao Wang

However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i. e., there could be different ways to ask a same question or different questions sharing similar expressions.

Community Question Answering

IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

1 code implementation3 Feb 2020 Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Haiqing Chen

We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.

Representation Learning

Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile

no code implementations9 Jul 2019 Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications.

Language Modelling

Controlling Risk of Web Question Answering

no code implementations24 May 2019 Lixin Su, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need.

Machine Reading Comprehension Question Answering

MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching

1 code implementation24 May 2019 Jiafeng Guo, Yixing Fan, Xiang Ji, Xue-Qi Cheng

Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation.

Information Retrieval Natural Language Processing +2

Outline Generation: Understanding the Inherent Content Structure of Documents

no code implementations24 May 2019 Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

To generate a sound outline, an ideal OG model should be able to capture three levels of coherence, namely the coherence between context paragraphs, that between a section and its heading, and that between context headings.

Structured Prediction

Tailored Sequence to Sequence Models to Different Conversation Scenarios

no code implementations ACL 2018 Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

In this paper, we propose two tailored optimization criteria for Seq2Seq to different conversation scenarios, i. e., the maximum generated likelihood for specific-requirement scenario, and the conditional value-at-risk for diverse-requirement scenario.

Dialogue Generation Response Generation

Modeling Diverse Relevance Patterns in Ad-hoc Retrieval

2 code implementations SIGIR '18 2018 Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, ChengXiang Zhai, Xue-Qi Cheng

The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document.

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

1 code implementation1 May 2018 Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen

Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

Knowledge Distillation Text Matching

A Tree Search Algorithm for Sequence Labeling

1 code implementation29 Apr 2018 Yadi Lao, Jun Xu, Yanyan Lan, Jiafeng Guo, Sheng Gao, Xue-Qi Cheng

Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word.

Chunking Decision Making +2

MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server

no code implementations22 Apr 2018 Guoxin Cui, Jun Xu, Wei Zeng, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng

One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchange the model gradients between the workers and servers during the training iterations.

Machine Learning Quantization +1

Unbiased Learning to Rank with Unbiased Propensity Estimation

1 code implementation16 Apr 2018 Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft

We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.

Learning-To-Rank online learning

Learning a Deep Listwise Context Model for Ranking Refinement

1 code implementation16 Apr 2018 Qingyao Ai, Keping Bi, Jiafeng Guo, W. Bruce Croft

Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results.

Information Retrieval Learning-To-Rank

aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model

1 code implementation5 Jan 2018 Liu Yang, Qingyao Ai, Jiafeng Guo, W. Bruce Croft

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers.

Feature Engineering Question Answering

Locally Smoothed Neural Networks

1 code implementation22 Nov 2017 Liang Pang, Yanyan Lan, Jun Xu, Jiafeng Guo, Xue-Qi Cheng

The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields.

Face Verification Question Answering +1

A Deep Investigation of Deep IR Models

no code implementations24 Jul 2017 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Therefore, it is necessary to identify the difference between automatically learned features by deep IR models and hand-crafted features used in traditional learning to rank approaches.

Information Retrieval Learning-To-Rank

MatchZoo: A Toolkit for Deep Text Matching

1 code implementation23 Jul 2017 Yixing Fan, Liang Pang, Jianpeng Hou, Jiafeng Guo, Yanyan Lan, Xue-Qi Cheng

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods.

Ad-Hoc Information Retrieval Information Retrieval +2

Spherical Paragraph Model

no code implementations18 Jul 2017 Ruqing Zhang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xue-Qi Cheng

Representing texts as fixed-length vectors is central to many language processing tasks.

Natural Language Processing Representation Learning +1

A Study of MatchPyramid Models on Ad-hoc Retrieval

1 code implementation15 Jun 2016 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it.

Machine Translation Paraphrase Identification +3

Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

1 code implementation15 Apr 2016 Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, Xue-Qi Cheng

In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i. e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position.

Semantic Regularities in Document Representations

no code implementations24 Mar 2016 Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, Xue-Qi Cheng

Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language.

Text Matching as Image Recognition

7 code implementations20 Feb 2016 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, Xue-Qi Cheng

An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.

Ad-Hoc Information Retrieval Natural Language Processing +1

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

1 code implementation26 Nov 2015 Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, Xue-Qi Cheng

Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.

Information Retrieval Question Answering +1

Stochastic Rank Aggregation

no code implementations26 Sep 2013 Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng

Traditional rank aggregation methods are deterministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized.

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