Search Results for author: Min Zhang

Found 147 papers, 43 papers with code

Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search

no code implementations22 Sep 2021 Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang, Shaoping Ma

Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i. e., content and context factors).

EEG

Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics

no code implementations1 Sep 2021 Xiaotian Jiang, Danshi Wang, Qirui Fan, Min Zhang, Chao Lu, Alan Pak Tao Lau

A physics-informed neural network (PINN) that combines deep learning with physics is studied to solve the nonlinear Schr\"odinger equation for learning nonlinear dynamics in fiber optics.

Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance

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

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

Information Retrieval Quantization

Exploiting Rich Syntax for Better Knowledge Base Question Answering

no code implementations16 Jul 2021 Pengju Zhang, Yonghui Jia, Muhua Zhu, Wenliang Chen, Min Zhang

Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees. In this paper, we propose an approach to learn syntax-based representations for KBQA.

Knowledge Base Question Answering

Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning

no code implementations13 Jun 2021 Peng Jin, Min Zhang, Jianwen Li, Li Han, Xuejun Wen

Formally verifying Deep Reinforcement Learning (DRL) systems is a challenging task due to the dynamic continuity of system behaviors and the black-box feature of embedded neural networks.

A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing

1 code implementation11 Jun 2021 Bin Hao, Min Zhang, Weizhi Ma, Shaoyun Shi, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma

To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.

Gender Prediction

Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction

1 code implementation6 Jun 2021 Jinpeng Zhang, Baijun Ji, Nini Xiao, Xiangyu Duan, Min Zhang, Yangbin Shi, Weihua Luo

Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces.

Bilingual Lexicon Induction Word Embeddings

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents

1 code implementation NAACL 2021 Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.

Multi-Task Learning Opinion Mining

An In-depth Study on Internal Structure of Chinese Words

1 code implementation ACL 2021 Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan

Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.

Optimizing Dense Retrieval Model Training with Hard Negatives

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

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

Information Retrieval Representation Learning

Not All Attention Is All You Need

no code implementations10 Apr 2021 Hongqiu Wu, Hai Zhao, Min Zhang

Beyond the success story of pre-trained language models (PrLMs) in recent natural language processing, they are susceptible to over-fitting due to unusual large model size.

Document Classification Named Entity Recognition +1

Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

no code implementations17 Mar 2021 Juntao Li, Chang Liu, Chongyang Tao, Zhangming Chan, Dongyan Zhao, Min Zhang, Rui Yan

To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).

Representation Learning

R-PointHop: A Green, Accurate and Unsupervised Point Cloud Registration Method

1 code implementation15 Mar 2021 Pranav Kadam, Min Zhang, Shan Liu, C. -C. Jay Kuo

The use of LRF allows for hierarchical features of points to be invariant with respect to rotation and translation, thus making R-PointHop more robust in building point correspondence even when rotation angles are large.

Dimensionality Reduction Point Cloud Registration

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

1 code implementation ACL 2021 Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou

Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.

Document Image Classification Language Modelling +1

Code Summarization with Structure-induced Transformer

1 code implementation29 Dec 2020 Hongqiu Wu, Hai Zhao, Min Zhang

Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing.

Code Summarization Natural Language Understanding +1

THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment

no code implementations24 Dec 2020 Yunqiu Shao, Bulou Liu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

We participated in the two case law tasks, i. e., the legal case retrieval task and the legal case entailment task.

Semantic Role Labeling with Heterogeneous Syntactic Knowledge

1 code implementation COLING 2020 Qingrong Xia, Rui Wang, Zhenghua Li, Yue Zhang, Min Zhang

Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention.

Semantic Role Labeling

Multi-grained Chinese Word Segmentation with Weakly Labeled Data

no code implementations COLING 2020 Chen Gong, Zhenghua Li, Bowei Zou, Min Zhang

Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1. 12 and 5. 97 on NEWS and BAIKE data in F1.

Chinese Word Segmentation

Improving Relation Extraction with Relational Paraphrase Sentences

1 code implementation COLING 2020 Junjie Yu, Tong Zhu, Wenliang Chen, Wei zhang, Min Zhang

In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.

Relation Extraction

Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations

no code implementations COLING 2020 Ying Li, Zhenghua Li, Min Zhang

The major challenge for current parsing research is to improve parsing performance on out-of-domain texts that are very different from the in-domain training data when there is only a small-scale out-domain labeled data.

Dependency Parsing Domain Adaptation +1

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction

1 code implementation COLING 2020 Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei zhang, Min Zhang

The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.

Relation Extraction

Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently

2 code implementations20 Oct 2020 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

Through this process, it teaches the DR model how to retrieve relevant documents from the entire corpus instead of how to rerank a potentially biased sample of documents.

Information Retrieval Passage Retrieval

Tight Lower Complexity Bounds for Strongly Convex Finite-Sum Optimization

no code implementations17 Oct 2020 Min Zhang, Yao Shu, Kun He

Finite-sum optimization plays an important role in the area of machine learning, and hence has triggered a surge of interest in recent years.

Improving AMR Parsing with Sequence-to-Sequence Pre-training

1 code implementation EMNLP 2020 Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.

AMR Parsing Machine Translation +1

A new dataset of dog breed images and a benchmark for fine-grained classification

1 code implementation1 Oct 2020 Ding-Nan Zo, Song-Hai Zhang, Tai-Jiang M, Min Zhang

It is currently the largest dataset for fine-grained classification of dogs, including130 dog breeds and 70, 428 real-world images.

Classification Fine-Grained Image Classification +2

Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition

1 code implementation10 Sep 2020 Siteng Huang, Min Zhang, Yachen Kang, Donglin Wang

However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation.

Feature Selection Metric Learning

Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation

no code implementations2 Sep 2020 Min Zhang, Pranav Kadam, Shan Liu, C. -C. Jay Kuo

The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture.

Classification General Classification +1

Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)

no code implementations2 Sep 2020 Pranav Kadam, Min Zhang, Shan Liu, C. -C. Jay Kuo

An unsupervised point cloud registration method, called salient points analysis (SPA), is proposed in this work.

Point Cloud Registration

Neural Logic Reasoning

1 code implementation20 Aug 2020 Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang

Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.

Recommendation Systems

Accelerating Robustness Verification of Deep Neural Networks Guided by Target Labels

no code implementations16 Jul 2020 Wenjie Wan, Zhaodi Zhang, Yiwei Zhu, Min Zhang, Fu Song

The key insight of our approach is that the robustness verification problem of DNNs can be solved by verifying sub-problems of DNNs, one per target label.

Autonomous Driving Medical Diagnosis

Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences

1 code implementation ACL 2020 Xiangyu Duan, Baijun Ji, Hao Jia, Min Tan, Min Zhang, Boxing Chen, Weihua Luo, Yue Zhang

In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.

Machine Translation

Aspect Sentiment Classification with Document-level Sentiment Preference Modeling

no code implementations ACL 2020 Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou

This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.

Classification Document-level +3

Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks

no code implementations ACL 2020 Bo Zhang, Yue Zhang, Rui Wang, Zhenghua Li, Min Zhang

The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9. 29 over the syntax-agnostic baseline.

Fine-Grained Opinion Analysis Multi-Task Learning

Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation

2 code implementations1 Jul 2020 Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma

However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.

Knowledge Graph Embedding Knowledge Graphs +1

RepBERT: Contextualized Text Embeddings for First-Stage Retrieval

1 code implementation28 Jun 2020 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings.

Efficient Second-Order TreeCRF for Neural Dependency Parsing

2 code implementations ACL 2020 Yu Zhang, Zhenghua Li, Min Zhang

Experiments and analysis on 27 datasets from 13 languages clearly show that techniques developed before the DL era, such as structural learning (global TreeCRF loss) and high-order modeling are still useful, and can further boost parsing performance over the state-of-the-art biaffine parser, especially for partially annotated training data.

Chinese Dependency Parsing Dependency Parsing

Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?

no code implementations6 Mar 2020 Houquan Zhou, Yu Zhang, Zhenghua Li, Min Zhang

In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing.

Dependency Parsing Feature Engineering +2

PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

2 code implementations9 Feb 2020 Min Zhang, Yifan Wang, Pranav Kadam, Shan Liu, C. -C. Jay Kuo

The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.

3D Classification 3D Point Cloud Classification +3

Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem

no code implementations22 Jan 2020 Kun He, Min Zhang, Jianrong Zhou, Yan Jin, Chu-min Li

Inspired by its success in deep learning, we apply the idea of SGD with batch selection of samples to a classic optimization problem in decision version.

AE-OT-GAN: Training GANs from data specific latent distribution

no code implementations ECCV 2020 Dongsheng An, Yang Guo, Min Zhang, Xin Qi, Na lei, Shing-Tung Yau, Xianfeng GU

Though generative adversarial networks (GANs) areprominent models to generate realistic and crisp images, they often encounter the mode collapse problems and arehard to train, which comes from approximating the intrinsicdiscontinuous distribution transform map with continuousDNNs.

Knowledge Graph Embedding via Graph Attenuated Attention Networks

no code implementations IEEE Access 2019 Rui Wang, Bicheng Li, Shengwei Hu, Wenqian Du, Min Zhang

However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features.

Knowledge Base Completion Knowledge Graph Completion +3

Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation

no code implementations3 Dec 2019 Baijun Ji, Zhirui Zhang, Xiangyu Duan, Min Zhang, Boxing Chen, Weihua Luo

However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side.

Machine Translation Transfer Learning

Improving Neural Relation Extraction with Positive and Unlabeled Learning

no code implementations28 Nov 2019 Zhengqiu He, Wenliang Chen, Yuyi Wang, Wei zhang, Guanchun Wang, Min Zhang

We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning.

Relation Extraction

Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification

no code implementations20 Nov 2019 Huan Zhang, Zhao Zhang, Mingbo Zhao, Qiaolin Ye, Min Zhang, Meng Wang

Our method can jointly re-cover the underlying clean data, clean labels and clean weighting spaces by decomposing the original data, predicted soft labels or weights into a clean part plus an error part by fitting noise.

General Classification

SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT

no code implementations CONLL 2019 Yue Zhang, Wei Jiang, Qingrong Xia, Junjie Cao, Rui Wang, Zhenghua Li, Min Zhang

Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.

Multi-Task Learning POS

Contrastive Attention Mechanism for Abstractive Sentence Summarization

1 code implementation IJCNLP 2019 Xiangyu Duan, Hoongfei Yu, Mingming Yin, Min Zhang, Weihua Luo, Yue Zhang

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence.

Abstractive Text Summarization Sentence Summarization

Neural Logic Networks

no code implementations17 Oct 2019 Shaoyun Shi, Hanxiong Chen, Min Zhang, Yongfeng Zhang

The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning.

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation

1 code implementation IJCNLP 2019 Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou

Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.

AMR-to-Text Generation Text Generation

CCKS 2019 Shared Task on Inter-Personal Relationship Extraction

1 code implementation29 Aug 2019 Haitao Wang, Zhengqiu He, Tong Zhu, Hao Shao, Wenliang Chen, Min Zhang

In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.

Emotion Detection with Neural Personal Discrimination

no code implementations IJCNLP 2019 Xiabing Zhou, Zhongqing Wang, Shoushan Li, Guodong Zhou, Min Zhang

Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions.

PointHop: An Explainable Machine Learning Method for Point Cloud Classification

3 code implementations30 Jul 2019 Min Zhang, Haoxuan You, Pranav Kadam, Shan Liu, C. -C. Jay Kuo

In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.

Classification General Classification +1

Syntax-aware Neural Semantic Role Labeling

1 code implementation22 Jul 2019 Qingrong Xia, Zhenghua Li, Min Zhang, Meishan Zhang, Guohong Fu, Rui Wang, Luo Si

Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.

Semantic Parsing Semantic Role Labeling

Sentence-Level Agreement for Neural Machine Translation

no code implementations ACL 2019 Mingming Yang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Min Zhang, Tiejun Zhao

The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.

Machine Translation

Semi-supervised Domain Adaptation for Dependency Parsing

1 code implementation ACL 2019 Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, Luo Si

During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data.

Chinese Dependency Parsing Dependency Parsing +2

Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network

no code implementations ACL 2019 Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.

Classification General Classification +2

Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention

1 code implementation ACL 2019 Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, Weihua Luo

But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system.

Sentence Summarization

HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing

no code implementations SEMEVAL 2019 Wei Jiang, Zhenghua Li, Yu Zhang, Min Zhang

The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.

General Classification Multi-Task Learning +1

Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier

no code implementations27 May 2019 Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Yan

Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers.

Dictionary Learning General Classification

HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing

no code implementations11 Mar 2019 Wei Jiang, Zhenghua Li, Yu Zhang, Min Zhang

The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.

General Classification UCCA Parsing

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

1 code implementation9 Mar 2019 Weizhi Ma, Min Zhang, Yue Cao, Woojeong, Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren

The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.

Knowledge Graphs Recommendation Systems

A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm

no code implementations20 Feb 2019 Lu Tan, Ling Li, Wanquan Liu, Jie Sun, Min Zhang

Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images.

Semi-supervised learning via Feedforward-Designed Convolutional Neural Networks

no code implementations6 Feb 2019 Yueru Chen, Yijing Yang, Min Zhang, C. -C. Jay Kuo

A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work.

General Classification Image Classification

Improving the Transformer Translation Model with Document-Level Context

3 code implementations EMNLP 2018 Jiacheng Zhang, Huanbo Luan, Maosong Sun, FeiFei Zhai, Jingfang Xu, Min Zhang, Yang Liu

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.

Document-level

Interpretable Convolutional Neural Networks via Feedforward Design

2 code implementations5 Oct 2018 C. -C. Jay Kuo, Min Zhang, Siyang Li, Jiali Duan, Yueru Chen

To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform.

Adaptive Weighting for Neural Machine Translation

1 code implementation COLING 2018 Yachao Li, Junhui Li, Min Zhang

In the popular sequence to sequence (seq2seq) neural machine translation (NMT), there exist many weighted sum models (WSMs), each of which takes a set of input and generates one output.

Machine Translation

NEWS 2018 Whitepaper

no code implementations WS 2018 Nancy Chen, Xiangyu Duan, Min Zhang, Rafael E. Banchs, Haizhou Li

Transliteration is defined as phonetic translation of names across languages.

Transliteration

Report of NEWS 2018 Named Entity Transliteration Shared Task

no code implementations WS 2018 Nancy Chen, Rafael E. Banchs, Min Zhang, Xiangyu Duan, Haizhou Li

This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia.

Information Retrieval Transliteration

Supervised Treebank Conversion: Data and Approaches

no code implementations ACL 2018 Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si

Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.

Dependency Parsing Multi-Task Learning

Classification of lung nodules in CT images based on Wasserstein distance in differential geometry

no code implementations30 Jun 2018 Min Zhang, Qianli Ma, Chengfeng Wen, Hai Chen, Deruo Liu, Xianfeng GU, Jie He, Xiaoyin Xu

The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods.

Computed Tomography (CT) General Classification +1

Adversarial Learning for Chinese NER from Crowd Annotations

no code implementations16 Jan 2018 YaoSheng Yang, Meishan Zhang, Wenliang Chen, Wei zhang, Haofen Wang, Min Zhang

To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.

Chinese Named Entity Recognition NER

SEE: Syntax-aware Entity Embedding for Neural Relation Extraction

no code implementations11 Jan 2018 Zhengqiu He, Wenliang Chen, Zhenghua Li, Meishan Zhang, Wei zhang, Min Zhang

First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.

Relation Classification Sentence Embedding

Improved English to Russian Translation by Neural Suffix Prediction

no code implementations11 Jan 2018 Kai Song, Yue Zhang, Min Zhang, Weihua Luo

Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages.

Machine Translation

Multi-Grained Chinese Word Segmentation

no code implementations EMNLP 2017 Chen Gong, Zhenghua Li, Min Zhang, Xinzhou Jiang

Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence.

Chinese Word Segmentation Language Modelling

Modeling Source Syntax for Neural Machine Translation

no code implementations ACL 2017 Junhui Li, Deyi Xiong, Zhaopeng Tu, Muhua Zhu, Min Zhang, Guodong Zhou

Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements.

Machine Translation

Improving Statistical Machine Translation with Selectional Preferences

no code implementations COLING 2016 Haiqing Tang, Deyi Xiong, Min Zhang, ZhengXian Gong

In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the context of machine translation.

Machine Translation Semantic Role Labeling +1

Learning Event Expressions via Bilingual Structure Projection

no code implementations COLING 2016 Fangyuan Li, Ruihong Huang, Deyi Xiong, Min Zhang

Aiming to resolve high complexities of event descriptions, previous work (Huang and Riloff, 2013) proposes multi-faceted event recognition and a bootstrapping method to automatically acquire both event facet phrases and event expressions from unannotated texts.

Distributed Representations for Building Profiles of Users and Items from Text Reviews

no code implementations COLING 2016 Wenliang Chen, Zhenjie Zhang, Zhenghua Li, Min Zhang

In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems.

Decision Making Matrix Completion +2

Neural Machine Translation Advised by Statistical Machine Translation

no code implementations17 Oct 2016 Xing Wang, Zhengdong Lu, Zhaopeng Tu, Hang Li, Deyi Xiong, Min Zhang

Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years.

Machine Translation

Training Dependency Parsers with Partial Annotation

no code implementations29 Sep 2016 Zhenghua Li, Yue Zhang, Jiayuan Chao, Min Zhang

The first approach is previously proposed to directly train a log-linear graph-based parser (LLGPar) with PA based on a forest-based objective.

Dependency Parsing

Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data

no code implementations4 Aug 2016 Qingrong Xia, Zhenghua Li, Jiayuan Chao, Min Zhang

This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.

Variational Neural Machine Translation

1 code implementation EMNLP 2016 Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang

Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence.

Machine Translation

Variational Neural Discourse Relation Recognizer

1 code implementation EMNLP 2016 Biao Zhang, Deyi Xiong, Jinsong Su, Qun Liu, Rongrong Ji, Hong Duan, Min Zhang

In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.

Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification

no code implementations11 Feb 2015 Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma

In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis.

Classification General Classification +1

Minimum $n$-Rank Approximation via Iterative Hard Thresholding

no code implementations18 Nov 2013 Min Zhang, Lei Yang, Zheng-Hai Huang

Additionally, combining an effective heuristic for determining $n$-rank, we can also apply the proposed algorithm to solve MnRA when $n$-rank is unknown in advance.

Image Inpainting Video Inpainting

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