Search Results for author: Yaliang Li

Found 73 papers, 40 papers with code

Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness

no code implementations EMNLP 2021 Hengtong Zhang, Tianhang Zheng, Yaliang Li, Jing Gao, Lu Su, Bo Li

To address this problem, we propose a training framework with certified robustness to eliminate the causes that trigger the generation of profanity.

Dialogue Generation Style Transfer

Wasserstein Selective Transfer Learning for Cross-domain Text Mining

no code implementations EMNLP 2021 Lingyun Feng, Minghui Qiu, Yaliang Li, Haitao Zheng, Ying Shen

However, the source and target domains usually have different data distributions, which may lead to negative transfer.

Transfer Learning

Tunable Soft Prompts are Messengers in Federated Learning

1 code implementation12 Nov 2023 Chenhe Dong, Yuexiang Xie, Bolin Ding, Ying Shen, Yaliang Li

As the global model itself is not required to be shared and the local training is conducted based on an auxiliary model with fewer parameters than the global model, the proposed approach provides protection for the global model while reducing communication and computation costs in FL.

Federated Learning Language Modelling +1

FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

1 code implementation1 Sep 2023 Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou

When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.

Benchmarking Federated Learning +1

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

1 code implementation29 Aug 2023 Dawei Gao, Haibin Wang, Yaliang Li, Xiuyu Sun, Yichen Qian, Bolin Ding, Jingren Zhou

Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning.

Prompt Engineering Text-To-SQL

TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

no code implementations16 Aug 2023 Chenxi Sun, Yaliang Li, Hongyan Li, Shenda Hong

Considering the insufficient data accumulation, limited resources, and semantic context requirements, this work focuses on TS-for-LLM methods, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM.

Language Modelling Test +1

Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study

1 code implementation16 Jul 2023 Peiyu Liu, Zikang Liu, Ze-Feng Gao, Dawei Gao, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen

Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models.

Instruction Following Quantization

Counterfactual Debiasing for Generating Factually Consistent Text Summaries

no code implementations18 May 2023 Chenhe Dong, Yuexiang Xie, Yaliang Li, Ying Shen

Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved.

Abstractive Text Summarization

Multi-grained Hypergraph Interest Modeling for Conversational Recommendation

1 code implementation4 May 2023 Chenzhan Shang, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Jing Zhang

In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations.

Recommendation Systems

Efficient Personalized Federated Learning via Sparse Model-Adaptation

2 code implementations4 May 2023 Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li

To overcome these challenges, we propose a novel approach named pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models.

Personalized Federated Learning

HPN: Personalized Federated Hyperparameter Optimization

no code implementations11 Apr 2023 Anda Cheng, Zhen Wang, Yaliang Li, Jian Cheng

The client encoding is calculated with a random projection-based procedure to protect each client's privacy.

Federated Learning Hyperparameter Optimization

LON-GNN: Spectral GNNs with Learnable Orthonormal Basis

1 code implementation24 Mar 2023 Qian Tao, Zhen Wang, Wenyuan Yu, Yaliang Li, Zhewei Wei

In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks.

FS-Real: Towards Real-World Cross-Device Federated Learning

no code implementations23 Mar 2023 Daoyuan Chen, Dawei Gao, Yuexiang Xie, Xuchen Pan, Zitao Li, Yaliang Li, Bolin Ding, Jingren Zhou

Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry.

Federated Learning

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

1 code implementation3 Feb 2023 Zeyu Qin, Liuyi Yao, Daoyuan Chen, Yaliang Li, Bolin Ding, Minhao Cheng

We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments.

Backdoor Attack Personalized Federated Learning

Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning

1 code implementation12 Dec 2022 Chenhe Dong, Yuexiang Xie, Bolin Ding, Ying Shen, Yaliang Li

In this study, we further broaden the application scope of FL in NLP by proposing an Assign-Then-Contrast (denoted as ATC) framework, which enables clients with heterogeneous NLP tasks to construct an FL course and learn useful knowledge from each other.

Federated Learning Natural Language Understanding +1

Privacy-Preserved Neural Graph Similarity Learning

1 code implementation21 Oct 2022 Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years.

Graph Matching Graph Similarity +1

Towards Universal Sequence Representation Learning for Recommender Systems

1 code implementation13 Jun 2022 Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen

In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors.

Recommendation Systems Representation Learning

pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

1 code implementation8 Jun 2022 Daoyuan Chen, Dawei Gao, Weirui Kuang, Yaliang Li, Bolin Ding

Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients.

Fairness Personalized Federated Learning

FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization

1 code implementation8 Jun 2022 Zhen Wang, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li

Due to this uniqueness, existing HPO benchmarks no longer satisfy the need to compare HPO methods in the FL setting.

Benchmarking Federated Learning +1

A Benchmark for Federated Hetero-Task Learning

1 code implementation7 Jun 2022 Liuyi Yao, Dawei Gao, Zhen Wang, Yuexiang Xie, Weirui Kuang, Daoyuan Chen, Haohui Wang, Chenhe Dong, Bolin Ding, Yaliang Li

To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.

Federated Learning Meta-Learning +2

ID-Agnostic User Behavior Pre-training for Sequential Recommendation

no code implementations6 Jun 2022 Shanlei Mu, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Bolin Ding

Instead of explicitly learning representations for item IDs, IDA-SR directly learns item representations from rich text information.

Language Modelling Sequential Recommendation

EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks

1 code implementation27 May 2022 Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei

Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels.

Node Classification

FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning

1 code implementation12 Apr 2022 Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou

The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications.

Federated Learning Graph Learning

FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

1 code implementation11 Apr 2022 Yuexiang Xie, Zhen Wang, Dawei Gao, Daoyuan Chen, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, Jingren Zhou

Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals.

Federated Learning Hyperparameter Optimization

Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling

1 code implementation27 Dec 2021 Yang Deng, Yaliang Li, Wenxuan Zhang, Bolin Ding, Wai Lam

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience.

Answer Generation Question Answering

HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression

1 code implementation EMNLP 2021 Chenhe Dong, Yaliang Li, Ying Shen, Minghui Qiu

In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information.

Few-Shot Learning Knowledge Distillation +2

Learned Index with Dynamic $\epsilon$

no code implementations29 Sep 2021 Daoyuan Chen, Wuchao Li, Yaliang Li, Bolin Ding, Kai Zeng, Defu Lian, Jingren Zhou

We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method.

Retrieval

Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning

no code implementations29 Sep 2021 Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, Jing Gao

To tackle these challenges, we propose a novel casual graph based fair prediction framework, which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph.

Fairness Graph structure learning

iFlood: A Stable and Effective Regularizer

no code implementations ICLR 2022 Yuexiang Xie, Zhen Wang, Yaliang Li, Ce Zhang, Jingren Zhou, Bolin Ding

However, our further studies uncover that the design of the loss function of Flooding can lead to a discrepancy between its objective and implementation, and cause the instability issue.

Image Classification

Coarformer: Transformer for large graph via graph coarsening

no code implementations29 Sep 2021 Weirui Kuang, Zhen Wang, Yaliang Li, Zhewei Wei, Bolin Ding

We get rid of these obstacles by exploiting the complementary natures of GNN and Transformer, and trade the fine-grained long-range information for the efficiency of Transformer.

Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation

1 code implementation Findings (EMNLP) 2021 Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding

However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice.

Abstractive Text Summarization

VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition

3 code implementations19 Jul 2021 Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang, Bin Cui

End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.

AutoML Feature Engineering +1

Automated Graph Learning via Population Based Self-Tuning GCN

no code implementations9 Jul 2021 Ronghang Zhu, Zhiqiang Tao, Yaliang Li, Sheng Li

Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.

Graph Classification Graph Learning +3

Differential Privacy for Text Analytics via Natural Text Sanitization

1 code implementation Findings (ACL) 2021 Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, Sherman S. M. Chow

The sanitized texts also contribute to our sanitization-aware pretraining and fine-tuning, enabling privacy-preserving natural language processing over the BERT language model with promising utility.

Language Modelling Privacy Preserving

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

no code implementations20 May 2021 Yang Deng, Yaliang Li, Fei Sun, Bolin Ding, Wai Lam

However, existing methods mainly target at solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components, which restrict the scalability and generality of CRS and fall short of preserving a stable training procedure.

Decision Making Recommendation Systems +2

A Unified Transferable Model for ML-Enhanced DBMS

1 code implementation6 May 2021 Ziniu Wu, Pei Yu, Peilun Yang, Rong Zhu, Yuxing Han, Yaliang Li, Defu Lian, Kai Zeng, Jingren Zhou

We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.

Management

Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge

no code implementations12 Apr 2021 Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen

Answer selection, which is involved in many natural language processing applications such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge.

Answer Selection Representation Learning

Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation

no code implementations20 Jan 2021 Lingyun Feng, Minghui Qiu, Yaliang Li, Hai-Tao Zheng, Ying Shen

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be deployed in real-time applications.

Knowledge Distillation

A Pluggable Learned Index Method via Sampling and Gap Insertion

no code implementations4 Jan 2021 Yaliang Li, Daoyuan Chen, Bolin Ding, Kai Zeng, Jingren Zhou

In this paper, we propose a formal machine learning based framework to quantify the index learning objective, and study two general and pluggable techniques to enhance the learning efficiency and learning effectiveness for learned indexes.

BIG-bench Machine Learning Retrieval

Learning to Mutate with Hypergradient Guided Population

no code implementations NeurIPS 2020 Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu

Computing the gradient of model hyperparameters, i. e., hypergradient, enables a promising and natural way to solve the hyperparameter optimization task.

Hyperparameter Optimization

EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications

2 code implementations18 Nov 2020 Minghui Qiu, Peng Li, Chengyu Wang, Hanjie Pan, Ang Wang, Cen Chen, Xianyan Jia, Yaliang Li, Jun Huang, Deng Cai, Wei Lin

The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.

Compiler Optimization Conversational Question Answering +1

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

1 code implementation3 Nov 2020 Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, Ji-Rong Wen

In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.

Collaborative Filtering Sequential Recommendation

Scalable Graph Neural Networks via Bidirectional Propagation

1 code implementation NeurIPS 2020 Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen

Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.

Graph Sampling

FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data

no code implementations29 Jul 2020 Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei. Lin, Jingren Zhou

Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph.

Recommendation Systems

Simple and Deep Graph Convolutional Networks

5 code implementations ICML 2020 Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li

We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.

Graph Classification Graph Regression +3

Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents

no code implementations ACL 2020 Daoyuan Chen, Yaliang Li, Kai Lei, Ying Shen

Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts.

Relation Extraction

Practical Data Poisoning Attack against Next-Item Recommendation

no code implementations7 Apr 2020 Hengtong Zhang, Yaliang Li, Bolin Ding, Jing Gao

In real-world recommendation systems, the cost of retraining recommendation models is high, and the interaction frequency between users and a recommendation system is restricted. Given these real-world restrictions, we propose to let the agent interact with a recommender simulator instead of the target recommendation system and leverage the transferability of the generated adversarial samples to poison the target system.

Data Poisoning Recommendation Systems

A Survey on Causal Inference

1 code implementation5 Feb 2020 Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, Aidong Zhang

Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.

BIG-bench Machine Learning Causal Inference

AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search

1 code implementation13 Jan 2020 Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei. Lin, Jingren Zhou

Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.

Knowledge Distillation Neural Architecture Search

Automated Relational Meta-learning

1 code implementation ICLR 2020 Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li

In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.

Few-Shot Image Classification Meta-Learning

A Minimax Game for Instance based Selective Transfer Learning

no code implementations1 Jul 2019 Bo wang, Minghui Qiu, Xisen Wang, Yaliang Li, Yu Gong, Xiaoyi Zeng, Jung Huang, Bo Zheng, Deng Cai, Jingren Zhou

To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning.

Retrieval Text Retrieval +1

Multi-Grained Named Entity Recognition

1 code implementation ACL 2019 Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.

Multi-Grained Named Entity Recognition named-entity-recognition +4

Data Poisoning Attack against Knowledge Graph Embedding

no code implementations26 Apr 2019 Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, Kui Ren

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently.

Data Poisoning Knowledge Graph Completion +2

Entity Synonym Discovery via Multipiece Bilateral Context Matching

1 code implementation31 Dec 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.

Entity Disambiguation

Joint Slot Filling and Intent Detection via Capsule Neural Networks

3 code implementations ACL 2019 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.

Intent Detection Natural Language Understanding +1

Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering

2 code implementations6 Dec 2018 Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen

Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.

Answer Selection Knowledge Base Question Answering +1

Representation Learning for Treatment Effect Estimation from Observational Data

1 code implementation NeurIPS 2018 Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.

Causal Inference Representation Learning +1

Finding Similar Medical Questions from Question Answering Websites

no code implementations14 Oct 2018 Yaliang Li, Liuyi Yao, Nan Du, Jing Gao, Qi Li, Chuishi Meng, Chenwei Zhang, Wei Fan

Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.

Question Answering Retrieval

Towards Differentially Private Truth Discovery for Crowd Sensing Systems

no code implementations10 Oct 2018 Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, Bolin Ding

To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic.

Privacy Preserving

SynonymNet: Multi-context Bilateral Matching for Entity Synonyms

no code implementations27 Sep 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery.

MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data

no code implementations27 Sep 2018 Yang Deng, Yaliang Li, Ying Shen, Nan Du, Wei Fan, Min Yang, Kai Lei

In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation.

Databases

Cooperative Denoising for Distantly Supervised Relation Extraction

no code implementations COLING 2018 Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan, Ying Shen

Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts.

Denoising Information Retrieval +3

Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge

no code implementations COLING 2018 Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains.

Answer Selection Information Retrieval +2

Generative Discovery of Relational Medical Entity Pairs

no code implementations ICLR 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies.

Bringing Semantic Structures to User Intent Detection in Online Medical Queries

no code implementations22 Oct 2017 Chenwei Zhang, Nan Du, Wei Fan, Yaliang Li, Chun-Ta Lu, Philip S. Yu

The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.

Intent Detection Multi-Task Learning

Multi-source Hierarchical Prediction Consolidation

no code implementations11 Aug 2016 Chenwei Zhang, Sihong Xie, Yaliang Li, Jing Gao, Wei Fan, Philip S. Yu

We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.

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