Search Results for author: Sheng Zhang

Found 61 papers, 17 papers with code

Sociolectal Analysis of Pretrained Language Models

no code implementations EMNLP 2021 Sheng Zhang, Xin Zhang, Weiming Zhang, Anders Søgaard

Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes.

Pretrained Language Models

Locally Aggregated Feature Attribution on Natural Language Model Understanding

no code implementations22 Apr 2022 Sheng Zhang, Jin Wang, Haitao Jiang, Rui Song

Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result.

Language Modelling Sentiment Analysis

Machine learning predictions for local electronic properties of disordered correlated electron systems

no code implementations12 Apr 2022 Yi-Hsuan Liu, Sheng Zhang, Puhan Zhang, Ting-Kuo Lee, Gia-Wei Chern

We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems.

Variational Monte Carlo

Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning

1 code implementation ACL 2022 Miryam de Lhoneux, Sheng Zhang, Anders Søgaard

Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be surprisingly effective for cross-lingual transfer of syntactic parsing models (Wu and Dredze 2019), but only between related languages.

Cross-Lingual Transfer Dependency Parsing +2

FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing

1 code implementation ACL 2022 Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, Anders Søgaard

We present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks.

Fairness

Rule Mining over Knowledge Graphs via Reinforcement Learning

no code implementations21 Feb 2022 Lihan Chen, Sihang Jiang, Jingping Liu, Chao Wang, Sheng Zhang, Chenhao Xie, Jiaqing Liang, Yanghua Xiao, Rui Song

Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.

Knowledge Graphs reinforcement-learning

Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systems

no code implementations3 Jan 2022 Puhan Zhang, Sheng Zhang, Gia-Wei Chern

A general theory of the descriptor for the classical fields is formulated, and two types of models are distinguished depending on the presence or absence of an internal symmetry for the classical field.

Knowledge-Rich Self-Supervised Entity Linking

no code implementations15 Dec 2021 Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon

Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia.

Contrastive Learning Entity Linking

DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification

no code implementations9 Dec 2021 Yongbiao Chen, Sheng Zhang, Fangxin Liu, Chenggang Wu, Kaicheng Guo, Zhengwei Qi

Specifically, we directly constrain the output from the convolutional neural network to be discrete binary codes and ensure the learned binary codes are optimal for classification.

Vehicle Re-Identification

Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning

no code implementations NeurIPS 2021 Sheng Zhang, Zhe Zhang, Siva Theja Maguluri

The focus of this paper is on sample complexity guarantees of average-reward reinforcement learning algorithms, which are known to be more challenging to study than their discounted-reward counterparts.

Q-Learning

A Review on Graph Neural Network Methods in Financial Applications

no code implementations27 Nov 2021 Jianian Wang, Sheng Zhang, Yanghua Xiao, Rui Song

With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations.

Can depth-adaptive BERT perform better on binary classification tasks

no code implementations22 Nov 2021 Jing Fan, Xin Zhang, Sheng Zhang, Yan Pan, Lixiang Guo

In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations.

Classification Text Classification

Modular Self-Supervision for Document-Level Relation Extraction

no code implementations EMNLP 2021 Sheng Zhang, Cliff Wong, Naoto Usuyama, Sarthak Jain, Tristan Naumann, Hoifung Poon

Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications.

Reading Comprehension Relation Extraction

Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle

no code implementations27 Jul 2021 Jie Li, Sheng Zhang, Kai Han, Xia Yuan, Chunxia Zhao, Yu Liu

UGV-KPNet is computationally efficient with a small number of parameters and provides pixel-level accurate keypoints detection results in real-time.

Keypoint Detection

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

2 code implementations11 Jun 2021 Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.

Game of Poker Multi-agent Reinforcement Learning +1

Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations

no code implementations27 May 2021 Sheng Zhang, Puhan Zhang, Gia-Wei Chern

With the aid of modern machine learning methods, we demonstrate the first-ever large-scale kinetic Monte Carlo simulations of the phase separation process for the Falicov-Kimball model, which is one of the canonical strongly correlated electron systems.

TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval

no code implementations5 May 2021 Yongbiao Chen, Sheng Zhang, Fangxin Liu, Zhigang Chang, Mang Ye, Zhengwei Qi

Until now, the deep hashing for the image retrieval community has been dominated by convolutional neural network architectures, e. g. \texttt{Resnet}\cite{he2016deep}.

Image Retrieval

Joint Universal Syntactic and Semantic Parsing

1 code implementation12 Apr 2021 Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, Aaron Steven White, Benjamin Van Durme

While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other.

Semantic Parsing

L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation

no code implementations11 Feb 2021 Jiahao Xie, Sheng Zhang, Jianwei Lu, Ye Luo

Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation.

Medical Image Segmentation Semantic Segmentation

Experimental demonstration of memory-enhanced scaling for entanglement connection of quantum repeater segments

no code implementations21 Jan 2021 Yunfei Pu, Sheng Zhang, Yukai Wu, Nan Jiang, Wei Chang, Chang Li, Luming Duan

The experimental realization of entanglement connection of two quantum repeater segments with an efficient memory-enhanced scaling demonstrates a key advantage of the quantum repeater protocol, which makes a cornerstone towards future large-scale quantum networks.

Quantum Physics

A submetric characterization of Rolewicz's property ($β$)

no code implementations21 Jan 2021 Sheng Zhang

The main result is a submetric characterization of the class of Banach spaces admitting an equivalent norm with Rolewicz's property ($\beta$).

Functional Analysis

GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks

no code implementations1 Jan 2021 Sheng Zhang, Rui Song, Wenbin Lu

In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.

Worst-Case-Aware Curriculum Learning for Zero and Few Shot Transfer

1 code implementation23 Sep 2020 Sheng Zhang, Xin Zhang, Weiming Zhang, Anders Søgaard

Multi-task transfer learning based on pre-trained language encoders achieves state-of-the-art performance across a range of tasks.

Transfer Learning

Augmented Gaussian Random Field: Theory and Computation

no code implementations3 Sep 2020 Sheng Zhang, Xiu Yang, Samy Tindel, Guang Lin

We prove that under certain conditions, the observable and its derivatives of any order are governed by a single Gaussian random field, which is the aforementioned AGRF.

Statistics Theory Probability Statistics Theory

Universal Decompositional Semantic Parsing

no code implementations ACL 2020 Elias Stengel-Eskin, Aaron Steven White, Sheng Zhang, Benjamin Van Durme

We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores.

Semantic Parsing

Broad-Coverage Semantic Parsing as Transduction

no code implementations IJCNLP 2019 Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme

We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations.

AMR Parsing UCCA Parsing

Quantum Communication between Multiplexed Atomic Quantum Memories

no code implementations5 Sep 2019 Chang Li, Nan Jiang, Yukai Wu, Wei Chang, Yunfei Pu, Sheng Zhang, Lu-Ming Duan

The use of multiplexed atomic quantum memories (MAQM) can significantly enhance the efficiency to establish entanglement in a quantum network.

Quantum Physics

SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations

no code implementations17 Jul 2019 Sheng Zhang, Guang Lin

We demonstrate how to use our algorithm step by step and compare our algorithm with threshold sparse Bayesian regression (TSBR) for the discovery of differential equations.

Bayesian Inference

Neural Machine Reading Comprehension: Methods and Trends

no code implementations2 Jul 2019 Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang

Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years.

Machine Reading Comprehension

Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning

no code implementations27 May 2019 Zaiwei Chen, Sheng Zhang, Thinh T. Doan, John-Paul Clarke, Siva Theja Maguluri

To demonstrate the generality of our theoretical results on Markovian SA, we use it to derive the finite-sample bounds of the popular $Q$-learning with linear function approximation algorithm, under a condition on the behavior policy.

Q-Learning reinforcement-learning

AMR Parsing as Sequence-to-Graph Transduction

1 code implementation ACL 2019 Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme

Our experimental results outperform all previously reported SMATCH scores, on both AMR 2. 0 (76. 3% F1 on LDC2017T10) and AMR 1. 0 (70. 2% F1 on LDC2014T12).

AMR Parsing

Cross-lingual Decompositional Semantic Parsing

no code implementations EMNLP 2018 Sheng Zhang, Xutai Ma, Rachel Rudinger, Kevin Duh, Benjamin Van Durme

We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language.

Semantic Parsing

Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction

no code implementations SEMEVAL 2018 Hongyuan Mei, Sheng Zhang, Kevin Duh, Benjamin Van Durme

Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios.

TAG

Cross-lingual Semantic Parsing

no code implementations21 Apr 2018 Sheng Zhang, Kevin Duh, Benjamin Van Durme

We introduce the task of cross-lingual semantic parsing: mapping content provided in a source language into a meaning representation based on a target language.

Semantic Parsing

Neural-Davidsonian Semantic Proto-role Labeling

1 code implementation EMNLP 2018 Rachel Rudinger, Adam Teichert, Ryan Culkin, Sheng Zhang, Benjamin Van Durme

We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call "Neural-Davidsonian": predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence.

FPGA Implementations of 3D-SIMD Processor Architecture for Deep Neural Networks Using Relative Indexed Compressed Sparse Filter Encoding Format and Stacked Filters Stationary Flow

no code implementations28 Mar 2018 Yuechao Gao, Nianhong Liu, Sheng Zhang

It is a challenging task to deploy computationally and memory intensive State-of-the-art deep neural networks (DNNs) on embedded systems with limited hardware resources and power budgets.

Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks

1 code implementation23 Jan 2018 Yuechao Gao, Nianhong Liu, Sheng Zhang

To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding format, relative indexed compressed sparse filter format (CSF), to make the best of data sparsity, and simplify data handling at execution time.

Random Euler Complex-Valued Nonlinear Filters

no code implementations2 Jan 2018 Jiashu Zhang, Sheng Zhang, Defang Li

Over the last decade, both the neural network and kernel adaptive filter have successfully been used for nonlinear signal processing.

Feature Enhancement Network: A Refined Scene Text Detector

no code implementations12 Nov 2017 Sheng Zhang, Yuliang Liu, Lianwen Jin, Canjie Luo

In this paper, we propose a refined scene text detector with a \textit{novel} Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement.

Object Detection Region Proposal

Selective Decoding for Cross-lingual Open Information Extraction

no code implementations IJCNLP 2017 Sheng Zhang, Kevin Duh, Benjamin Van Durme

Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language.

Machine Translation Open Information Extraction

Deep Generalized Canonical Correlation Analysis

3 code implementations WS 2019 Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, Raman Arora

We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other.

Representation Learning Stochastic Optimization

Ordinal Common-sense Inference

no code implementations TACL 2017 Sheng Zhang, Rachel Rudinger, Kevin Duh, Benjamin Van Durme

Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly.

Common Sense Reasoning Natural Language Inference

Randomized Structural Sparsity based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multi-center Reproducibility Study

no code implementations7 Jun 2015 Yi-Lun Wang, Sheng Zhang, Junjie Zheng, Heng Chen, Huafu Chen

In this paper, we focus on how to locate the relevant or discriminative brain regions related with external stimulus or certain mental decease, which is also called support identification, based on the neuroimaging data.

Discrete Weierstrass Fourier Transform and Experiments

1 code implementation12 Feb 2015 Sheng Zhang, Brendan Harding

We established a new method called Discrete Weierstrass Fourier Transform, a faster and more generalized Discrete Fourier Transform, to approximate discrete data.

Numerical Analysis

Randomized Structural Sparsity via Constrained Block Subsampling for Improved Sensitivity of Discriminative Voxel Identification

no code implementations17 Oct 2014 Yi-Lun Wang, Junjie Zheng, Sheng Zhang, Xujun Duan, Huafu Chen

In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers.

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