Search Results for author: Xueqi Cheng

Found 111 papers, 56 papers with code

Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases

no code implementations COLING 2022 Kun Zhang, Yunqi Qiu, Yuanzhuo Wang, Long Bai, Wei Li, Xuhui Jiang, HuaWei Shen, Xueqi Cheng

Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints.

Contrastive Learning Meta-Learning +2

TEA: Test-time Energy Adaptation

no code implementations24 Nov 2023 Yige Yuan, Bingbing Xu, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

To address this, we propose a novel energy-based perspective, enhancing the model's perception of target data distributions without requiring access to training data or processes.


Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue

no code implementations13 Nov 2023 Junkai Zhou, Liang Pang, HuaWei Shen, Xueqi Cheng

The emergence of large language models (LLMs) further improves the capabilities of open-domain dialogue systems and can generate fluent, coherent, and diverse responses.

Dialogue Generation Prompt Engineering +1

CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval

no code implementations6 Nov 2023 Yinqiong Cai, Yixing Fan, Keping Bi, Jiafeng Guo, Wei Chen, Ruqing Zhang, Xueqi Cheng

The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently.


Retrieval-Augmented Code Generation for Universal Information Extraction

no code implementations6 Nov 2023 Yucan Guo, Zixuan Li, Xiaolong Jin, Yantao Liu, Yutao Zeng, Wenxuan Liu, Xiang Li, Pan Yang, Long Bai, Jiafeng Guo, Xueqi Cheng

Therefore, in this paper, we propose a universal retrieval-augmented code generation framework based on LLMs, called Code4UIE, for IE tasks.

Code Generation Retrieval

Plot Retrieval as an Assessment of Abstract Semantic Association

no code implementations3 Nov 2023 Shicheng Xu, Liang Pang, Jiangnan Li, Mo Yu, Fandong Meng, HuaWei Shen, Xueqi Cheng, Jie zhou

Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots.

Information Retrieval Retrieval

An In-Context Schema Understanding Method for Knowledge Base Question Answering

no code implementations22 Oct 2023 Yantao Liu, Zixuan Li, Xiaolong Jin, Long Bai, Saiping Guan, Jiafeng Guo, Xueqi Cheng

As a kind of common method for this task, semantic parsing-based ones first convert natural language questions to logical forms (e. g., SPARQL queries) and then execute them on knowledge bases to get answers.

Knowledge Base Question Answering Retrieval +1

From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

1 code implementation18 Oct 2023 Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng

We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence.

Fact Verification Retrieval

CIR at the NTCIR-17 ULTRE-2 Task

no code implementations18 Oct 2023 Lulu Yu, Keping Bi, Jiafeng Guo, Xueqi Cheng

The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task.

Information Retrieval Retrieval

RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling

no code implementations16 Oct 2023 Jingcheng Deng, Liang Pang, HuaWei Shen, Xueqi Cheng

It encodes the text corpus into a latent space, capturing current and future information from both source and target text.

Language Modelling Retrieval +1

Causality and Independence Enhancement for Biased Node Classification

1 code implementation14 Oct 2023 Guoxin Chen, Yongqing Wang, Fangda Guo, Qinglang Guo, Jiangli Shao, HuaWei Shen, Xueqi Cheng

Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias.

Classification Node Classification +1

Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

3 code implementations9 Oct 2023 Zezhi Shao, Fei Wang, Yongjun Xu, Wei Wei, Chengqing Yu, Zhao Zhang, Di Yao, Guangyin Jin, Xin Cao, Gao Cong, Christian S. Jensen, Xueqi Cheng

Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.

Benchmarking Multivariate Time Series Forecasting +1

A Topological Perspective on Demystifying GNN-Based Link Prediction Performance

1 code implementation6 Oct 2023 Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, Tyler Derr

Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors.

Link Prediction

A Comparative Study of Training Objectives for Clarification Facet Generation

1 code implementation1 Oct 2023 Shiyu Ni, Keping Bi, Jiafeng Guo, Xueqi Cheng

In this paper, we aim to conduct a systematic comparative study of various types of training objectives, with different properties of not only whether it is permutation-invariant but also whether it conducts sequential prediction and whether it can control the count of output facets.

Text Generation

ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

no code implementations22 Sep 2023 Zhilei Hu, Zixuan Li, Daozhu Xu, Long Bai, Cheng Jin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations.

Event Relation Extraction Relation Extraction

Nested Event Extraction upon Pivot Element Recogniton

no code implementations22 Sep 2023 Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng

Specifically, PerNee first recognizes the triggers of both inner and outer events and further recognizes the PEs via classifying the relation type between trigger pairs.

Event Extraction

Robust Recommender System: A Survey and Future Directions

no code implementations5 Sep 2023 Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, HuaWei Shen, Xueqi Cheng

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload.

Fairness Recommendation Systems +1

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

1 code implementation31 Aug 2023 Yi Zhang, Yuying Zhao, Zhaoqing Li, Xueqi Cheng, Yu Wang, Olivera Kotevska, Philip S. Yu, Tyler Derr

Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.

Privacy Preserving

Continual Learning for Generative Retrieval over Dynamic Corpora

no code implementations29 Aug 2023 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng

We put forward a novel Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major contributions to continual learning for GR: (i) To encode new documents into docids with low computational cost, we present Incremental Product Quantization, which updates a partial quantization codebook according to two adaptive thresholds; and (ii) To memorize new documents for querying without forgetting previous knowledge, we propose a memory-augmented learning mechanism, to form meaningful connections between old and new documents.

Continual Learning Quantization +1

Inducing Causal Structure for Abstractive Text Summarization

1 code implementation24 Aug 2023 Lu Chen, Ruqing Zhang, Wei Huang, Wei Chen, Jiafeng Guo, Xueqi Cheng

The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus.

Abstractive Text Summarization

L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations

1 code implementation22 Aug 2023 Yinqiong Cai, Keping Bi, Yixing Fan, Jiafeng Guo, Wei Chen, Xueqi Cheng

First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection.


Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method

no code implementations19 Aug 2023 Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng

The AREA task is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model in response to a query.

Adversarial Attack Contrastive Learning +1

Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer

no code implementations18 Aug 2023 Wendong Bi, Xueqi Cheng, Bingbing Xu, Xiaoqian Sun, Li Xu, HuaWei Shen

Transfer learning has been a feasible way to transfer knowledge from high-quality external data of source domains to limited data of target domains, which follows a domain-level knowledge transfer to learn a shared posterior distribution.

Retrieval Transfer Learning

OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning

1 code implementation21 Jul 2023 Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, HuaWei Shen, Xueqi Cheng

Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models.

Domain Adaptation Node Classification

Fairness and Diversity in Recommender Systems: A Survey

no code implementations10 Jul 2023 Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu Aggarwal, Tyler Derr

Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level.

Fairness Recommendation Systems

On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective

no code implementations22 Jun 2023 Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Xueqi Cheng

Recently, we have witnessed generative retrieval increasingly gaining attention in the information retrieval (IR) field, which retrieves documents by directly generating their identifiers.

Information Retrieval Retrieval

IDEA: Invariant Causal Defense for Graph Adversarial Robustness

no code implementations25 May 2023 Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Bingbing Xu, Xueqi Cheng

Through modeling and analyzing the causal relationships in graph adversarial attacks, we design two invariance objectives to learn the causal features.

Adversarial Robustness

PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion

no code implementations25 May 2023 Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones.

Data Augmentation

Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies

no code implementations24 May 2023 Yubao Tang, Ruqing Zhang, Jiafeng Guo, Jiangui Chen, Zuowei Zhu, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng

Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning.

Memorization Retrieval

MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space

1 code implementation22 May 2023 Hanxing Ding, Liang Pang, Zihao Wei, HuaWei Shen, Xueqi Cheng, Tat-Seng Chua

Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously.

Text Generation

Semantic Structure Enhanced Event Causality Identification

no code implementations22 May 2023 Zhilei Hu, Zixuan Li, Xiaolong Jin, Long Bai, Saiping Guan, Jiafeng Guo, Xueqi Cheng

This is a very challenging task, because causal relations are usually expressed by implicit associations between events.

Event Causality Identification

Popularity Debiasing from Exposure to Interaction in Collaborative Filtering

1 code implementation9 May 2023 YuanHao Liu, Qi Cao, HuaWei Shen, Yunfan Wu, Shuchang Tao, Xueqi Cheng

In this paper, we propose a new criterion for popularity debiasing, i. e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion.

Collaborative Filtering Recommendation Systems

Visual Transformation Telling

no code implementations3 May 2023 Xin Hong, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

In this paper, we propose a new visual reasoning task, called Visual Transformation Telling (VTT).

Dense Video Captioning Visual Reasoning +1

Visual Reasoning: from State to Transformation

1 code implementation2 May 2023 Xin Hong, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

Such \textbf{state driven} visual reasoning has limitations in reflecting the ability to infer the dynamics between different states, which has shown to be equally important for human cognition in Piaget's theory.

Visual Question Answering (VQA) Visual Reasoning

Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks

1 code implementation28 Apr 2023 Shicheng Xu, Liang Pang, HuaWei Shen, Xueqi Cheng, Tat-Seng Chua

Second, IR verifies the answer of each node of CoQ, it corrects the answer that is not consistent with the retrieved information when IR gives high confidence, which improves the credibility.

Fact Checking Information Retrieval +6

A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning

1 code implementation28 Apr 2023 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yiqun Liu, Yixing Fan, Xueqi Cheng

Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task.


Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models

1 code implementation28 Apr 2023 Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng

In this paper, we focus on a more general type of perturbation and introduce the topic-oriented adversarial ranking attack task against NRMs, which aims to find an imperceptible perturbation that can promote a target document in ranking for a group of queries with the same topic.

Information Retrieval Retrieval

Graph Adversarial Immunization for Certifiable Robustness

1 code implementation16 Feb 2023 Shuchang Tao, HuaWei Shen, Qi Cao, Yunfan Wu, Liang Hou, Xueqi Cheng

In this paper, we propose and formulate graph adversarial immunization, i. e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack.

Adversarial Attack Combinatorial Optimization

Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation

1 code implementation5 Feb 2023 JunJie Huang, Qi Cao, Ruobing Xie, Shaoliang Zhang, Feng Xia, HuaWei Shen, Xueqi Cheng

To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance.

Contrastive Learning Data Augmentation

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

1 code implementation2 Feb 2023 Wendong Bi, Bingbing Xu, Xiaoqian Sun, Li Xu, HuaWei Shen, Xueqi Cheng

To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes.

Representation Learning

Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks

1 code implementation31 Jan 2023 Wendong Bi, Bingbing Xu, Xiaoqian Sun, Zidong Wang, HuaWei Shen, Xueqi Cheng

However, most nodes in the tribe-style graph lack attributes, making it difficult to directly adopt existing graph learning methods (e. g., Graph Neural Networks(GNNs)).

Contrastive Learning Graph Learning

Multi-video Moment Ranking with Multimodal Clue

no code implementations29 Jan 2023 Danyang Hou, Liang Pang, Yanyan Lan, HuaWei Shen, Xueqi Cheng

In this paper, we focus on improving two problems of two-stage method: (1) Moment prediction bias: The predicted moments for most queries come from the top retrieved videos, ignoring the possibility that the target moment is in the bottom retrieved videos, which is caused by the inconsistency of Shared Normalization during training and inference.

Moment Retrieval Retrieval +1

Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding

no code implementations10 Jan 2023 Yunchang Zhu, Liang Pang, Kangxi Wu, Yanyan Lan, HuaWei Shen, Xueqi Cheng

Comparative loss is essentially a ranking loss on top of the task-specific losses of the full and ablated models, with the expectation that the task-specific loss of the full model is minimal.

Natural Language Understanding Network Pruning

Rich Event Modeling for Script Event Prediction

1 code implementation16 Dec 2022 Long Bai, Saiping Guan, Zixuan Li, Jiafeng Guo, Xiaolong Jin, Xueqi Cheng

Fundamentally, it is based on the proposed rich event description, which enriches the existing ones with three kinds of important information, namely, the senses of verbs, extra semantic roles, and types of participants.

NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework

no code implementations1 Dec 2022 Shicheng Xu, Liang Pang, HuaWei Shen, Xueqi Cheng

Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, etc., while they share the same schema to estimate the relationship between texts.

Information Retrieval Open-Domain Question Answering +1

Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective

no code implementations20 Nov 2022 Yige Yuan, Bingbing Xu, HuaWei Shen, Qi Cao, Keting Cen, Wen Zheng, Xueqi Cheng

Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks.

Contrastive Learning Data Augmentation +1

Visual Named Entity Linking: A New Dataset and A Baseline

1 code implementation9 Nov 2022 Wenxiang Sun, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng

Since each entity often contains rich visual and textual information in KBs, we thus propose three different sub-tasks, i. e., visual to visual entity linking (V2VEL), visual to textual entity linking (V2TEL), and visual to visual-textual entity linking (V2VTEL).

Entity Linking Image Retrieval +3

LegoNet: A Fast and Exact Unlearning Architecture

no code implementations28 Oct 2022 Sihao Yu, Fei Sun, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng

However, such a strategy typically leads to a loss in model performance, which poses the challenge that increasing the unlearning efficiency while maintaining acceptable performance.

Representation Learning

HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning

no code implementations18 Oct 2022 Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng

This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps.

Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models

1 code implementation14 Sep 2022 Chen Wu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Yixing Fan, Maarten de Rijke, Xueqi Cheng

A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack.

Information Retrieval Retrieval

Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models

no code implementations12 Sep 2022 Yinqiong Cai, Jiafeng Guo, Yixing Fan, Qingyao Ai, Ruqing Zhang, Xueqi Cheng

When sampling top-ranked results (excluding the labeled positives) as negatives from a stronger retriever, the performance of the learned NRM becomes even worse.

Information Retrieval Retrieval

A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval

no code implementations21 Aug 2022 Xinyu Ma, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng

Empirical results show that our method can significantly outperform the state-of-the-art autoencoder-based language models and other pre-trained models for dense retrieval.

Information Retrieval Retrieval

Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval

no code implementations21 Aug 2022 Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Xueqi Cheng

Unlike the promising results in NLP, we find that these methods cannot achieve comparable performance to full fine-tuning at both stages when updating less than 1\% of the original model parameters.

Information Retrieval Re-Ranking +1

CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks

1 code implementation16 Aug 2022 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yiqun Liu, Yixing Fan, Xueqi Cheng

We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning.


Adversarial Camouflage for Node Injection Attack on Graphs

1 code implementation3 Aug 2022 Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Liang Hou, Fei Sun, Xueqi Cheng

In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.

Learning node embeddings via summary graphs: a brief theoretical analysis

no code implementations4 Jul 2022 Houquan Zhou, Shenghua Liu, Danai Koutra, HuaWei Shen, Xueqi Cheng

Recent works try to improve scalability via graph summarization -- i. e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph.

Graph Mining Graph Representation Learning

Few-Shot Stance Detection via Target-Aware Prompt Distillation

no code implementations27 Jun 2022 Yan Jiang, Jinhua Gao, HuaWei Shen, Xueqi Cheng

The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets.

Few-Shot Learning Stance Detection

Augmentation-Aware Self-Supervision for Data-Efficient GAN Training

1 code implementation31 May 2022 Liang Hou, Qi Cao, Yige Yuan, Songtao Zhao, Chongyang Ma, Siyuan Pan, Pengfei Wan, Zhongyuan Wang, HuaWei Shen, Xueqi Cheng

Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting.

Data Augmentation Representation Learning

LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback

1 code implementation25 Apr 2022 Yunchang Zhu, Liang Pang, Yanyan Lan, HuaWei Shen, Xueqi Cheng

Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be.


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 +2

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 +1

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 +1

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

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.

Transductive Learning for Unsupervised Text Style Transfer

1 code implementation EMNLP 2021 Fei Xiao, Liang Pang, Yanyan Lan, Yan Wang, HuaWei Shen, Xueqi Cheng

The proposed transductive learning approach is general and effective to the task of unsupervised style transfer, and we will apply it to the other two typical methods in the future.

Retrieval Style Transfer +3

Single Node Injection Attack against Graph Neural Networks

1 code implementation30 Aug 2021 Shuchang Tao, Qi Cao, HuaWei Shen, JunJie Huang, Yunfan Wu, Xueqi Cheng

In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.

Signed Bipartite Graph Neural Networks

1 code implementation22 Aug 2021 JunJie Huang, HuaWei Shen, Qi Cao, Shuchang Tao, Xueqi Cheng

Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.

Link Sign Prediction Network Embedding

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 Retrieval +2

FedMatch: Federated Learning Over Heterogeneous Question Answering Data

2 code implementations11 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

Conditional GANs with Auxiliary Discriminative Classifier

2 code implementations21 Jul 2021 Liang Hou, Qi Cao, HuaWei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng

Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.

Conditional Image Generation

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 +1

INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering

1 code implementation12 Jul 2021 Yunfan Wu, Qi Cao, HuaWei Shen, Shuchang Tao, Xueqi Cheng

INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table.

Collaborative Filtering Recommendation Systems

Self-Supervised GANs with Label Augmentation

2 code implementations NeurIPS 2021 Liang Hou, HuaWei Shen, Qi Cao, Xueqi Cheng

Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment.

Data Augmentation Image Generation +2

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 (RL)

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

Locate Who You Are: Matching Geo-location to Text for User Identity Linkage

no code implementations19 Apr 2021 Jiangli Shao, Yongqing Wang, Hao Gao, HuaWei Shen, Yangyang Li, Xueqi Cheng

However, encouraged by online services, users would also post asymmetric information across networks, such as geo-locations and texts.

Anchor link prediction Marketing

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

GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction

no code implementations19 Mar 2021 Hao Gao, Yongqing Wang, Shanshan Lyu, HuaWei Shen, Xueqi Cheng

However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice.

Anchor link prediction

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 Retrieval +1

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 +2

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 Retrieval

Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching

1 code implementation16 Jan 2021 Liang Pang, Yanyan Lan, Xueqi Cheng

However, these models designed for short texts cannot well address the long-form text matching problem, because there are many contexts in long-form texts can not be directly aligned with each other, and it is difficult for existing models to capture the key matching signals from such noisy data.

Community Question Answering Information Retrieval +4

Modelling Universal Order Book Dynamics in Bitcoin Market

no code implementations15 Jan 2021 Fabin Shi, Nathan Aden, Shengda Huang, Neil Johnson, Xiaoqian Sun, Jinhua Gao, Li Xu, HuaWei Shen, Xueqi Cheng, Chaoming Song

Understanding the emergence of universal features such as the stylized facts in markets is a long-standing challenge that has drawn much attention from economists and physicists.

SDGNN: Learning Node Representation for Signed Directed Networks

1 code implementation7 Jan 2021 JunJie Huang, HuaWei Shen, Liang Hou, Xueqi Cheng

Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks.

Network Embedding

Towards Powerful Graph Neural Networks: Diversity Matters

no code implementations1 Jan 2021 Xu Bingbing, HuaWei Shen, Qi Cao, YuanHao Liu, Keting Cen, Xueqi Cheng

For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.

Graph Representation Learning Node Classification

Slimmable Generative Adversarial Networks

1 code implementation10 Dec 2020 Liang Hou, Zehuan Yuan, Lei Huang, HuaWei Shen, Xueqi Cheng, Changhu Wang

In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.

AugSplicing: Synchronized Behavior Detection in Streaming Tensors

1 code implementation3 Dec 2020 Jiabao Zhang, Shenghua Liu, Wenting Hou, Siddharth Bhatia, HuaWei Shen, Wenjian Yu, Xueqi Cheng

Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step.

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 (VQA) 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 +1

Summarizing graphs using configuration model

no code implementations19 Oct 2020 Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, HuaWei Shen, Xueqi Cheng

As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it.

Social and Information Networks

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

DeepHawkes: Bridging the gap between prediction and understanding of information cascades

1 code implementation CIKM 2017 Qi Cao, HuaWei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng

In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.

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