Search Results for author: Taifeng Wang

Found 26 papers, 7 papers with code

Incremental Event Detection via Knowledge Consolidation Networks

no code implementations EMNLP 2020 Pengfei Cao, Yubo Chen, Jun Zhao, Taifeng Wang

However, existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection.

Event Detection Incremental Learning

LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints

no code implementations27 Sep 2023 Weidi Xu, Jingwei Wang, Lele Xie, Jianshan He, Hongting Zhou, Taifeng Wang, Xiaopei Wan, Jingdong Chen, Chao Qu, Wei Chu

Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints.

Variational Inference

Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning

no code implementations14 Jan 2023 Zhihang Hu, Qinze Yu, Yucheng Guo, Taifeng Wang, Irwin King, Xin Gao, Le Song, Yu Li

While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines.

Graph structure learning

Document-level Event Extraction via Parallel Prediction Networks

2 code implementations ACL 2021 Hang Yang, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang

We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document.

Document-level Event Extraction Event Extraction +1

PairRE: Knowledge Graph Embeddings via Paired Relation Vectors

1 code implementation ACL 2021 Linlin Chao, Jianshan He, Taifeng Wang, Wei Chu

Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry.

Knowledge Graph Embedding Knowledge Graph Embeddings +2

Question Directed Graph Attention Network for Numerical Reasoning over Text

no code implementations EMNLP 2020 Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.

Graph Attention Machine Reading Comprehension +2

Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy

no code implementations ACL 2020 Xiexiong Lin, Weiyu Jian, Jianshan He, Taifeng Wang, Wei Chu

Experiments demonstrate that our model with fewer parameters yields significant improvements over competitive baselines on two datasets Wizard-of-Wikipedia(average Bleu +87{\%}; abs.

SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check

1 code implementation ACL 2020 Xingyi Cheng, Weidi Xu, Kunlong Chen, Shaohua Jiang, Feng Wang, Taifeng Wang, Wei Chu, Yuan Qi

This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).

Symmetric Regularization based BERT for Pair-wise Semantic Reasoning

1 code implementation8 Sep 2019 Weidi Xu, Xingyi Cheng, Kunlong Chen, Wei Wang, Bin Bi, Ming Yan, Chen Wu, Luo Si, Wei Chu, Taifeng Wang

To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).

Machine Reading Comprehension Natural Language Inference +2

BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction

1 code implementation16 Aug 2019 Weipeng Huang, Xingyi Cheng, Taifeng Wang, Wei Chu

Combining these three contributions, we enhance the information extracting ability of the multi-head selection model and achieve F1-score 0. 876 on testset-1 with a single model.

Relation Relation Extraction

Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer

no code implementations CONLL 2019 Xingyi Cheng, Weidi Xu, Taifeng Wang, Wei Chu

By disentangling the latent representation into the aspect-specific sentiment and the lexical context, our method induces the underlying sentiment prediction for the unlabeled data, which then benefits the ATSA classifier.

Aspect-Based Sentiment Analysis (ABSA) Natural Language Understanding +1

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

1 code implementation NeurIPS 2017 Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu

We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.

Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers

no code implementations1 Aug 2017 Quanming Yao, James T. Kwok, Taifeng Wang, Tie-Yan Liu

Based on it, we develop a proximal gradient algorithm (and its accelerated variant) with inexact proximal splitting and prove that a convergence rate of O(1/T) where T is the number of iterations is guaranteed.

Matrix Completion

A Communication-Efficient Parallel Algorithm for Decision Tree

no code implementations NeurIPS 2016 Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu

After partitioning the training data onto a number of (e. g., $M$) machines, this algorithm performs both local voting and global voting in each iteration.

2k Attribute

Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction

no code implementations27 Sep 2016 Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu

The results verified our theoretical findings and demonstrated the practical efficiency of the asynchronous stochastic proximal algorithms with variance reduction.

Generalization Error Bounds for Optimization Algorithms via Stability

no code implementations27 Sep 2016 Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu

Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG).

BIG-bench Machine Learning

Asynchronous Stochastic Gradient Descent with Delay Compensation

no code implementations ICML 2017 Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, Tie-Yan Liu

We propose a novel technology to compensate this delay, so as to make the optimization behavior of ASGD closer to that of sequential SGD.

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