Search Results for author: Xu sun

Found 145 papers, 63 papers with code

RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models

1 code implementation15 Oct 2021 Wenkai Yang, Yankai Lin, Peng Li, Jie zhou, Xu sun

Motivated by this observation, we construct a word-based robustness-aware perturbation to distinguish poisoned samples from clean samples to defend against the backdoor attacks on natural language processing (NLP) models.

Sentiment Analysis

Well-classified Examples are Underestimated in Classification with Deep Neural Networks

1 code implementation13 Oct 2021 Guangxiang Zhao, Wenkai Yang, Xuancheng Ren, Lei LI, Xu sun

The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary.

Classification Graph Classification +5

Topology-Imbalance Learning for Semi-Supervised Node Classification

1 code implementation8 Oct 2021 Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie zhou, Xu sun

The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community.

Classification Node Classification

Dynamic Knowledge Distillation for Pre-trained Language Models

1 code implementation23 Sep 2021 Lei LI, Yankai Lin, Shuhuai Ren, Peng Li, Jie zhou, Xu sun

Knowledge distillation~(KD) has been proved effective for compressing large-scale pre-trained language models.

Knowledge Distillation

Adversarial Parameter Defense by Multi-Step Risk Minimization

no code implementations7 Sep 2021 Zhiyuan Zhang, Ruixuan Luo, Xuancheng Ren, Qi Su, Liangyou Li, Xu sun

To enhance neural networks, we propose the adversarial parameter defense algorithm that minimizes the average risk of multiple adversarial parameter corruptions.

How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data

no code implementations3 Sep 2021 Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang, Lichao Sun, Xu sun

In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors.

Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification

1 code implementation1 Sep 2021 Shuhuai Ren, Jinchao Zhang, Lei LI, Xu sun, Jie zhou

Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations.

Classification Data Augmentation +1

ASAT: Adaptively Scaled Adversarial Training in Time Series

no code implementations20 Aug 2021 Zhiyuan Zhang, Wei Li, Ruihan Bao, Keiko Harimoto, Yunfang Wu, Xu sun

Besides the security concerns of potential adversarial examples, adversarial training can also improve the performance of the neural networks, train robust neural networks, and provide interpretability for neural networks.

Time Series Time Series Analysis

Rethinking Stealthiness of Backdoor Attack against NLP Models

1 code implementation ACL 2021 Wenkai Yang, Yankai Lin, Peng Li, Jie zhou, Xu sun

In this work, we point out a potential problem of current backdoor attacking research: its evaluation ignores the stealthiness of backdoor attacks, and most of existing backdoor attacking methods are not stealthy either to system deployers or to system users.

Data Augmentation Sentiment Analysis +1

Contrastive Attention for Automatic Chest X-ray Report Generation

no code implementations13 Jun 2021 Fenglin Liu, Changchang Yin, Xian Wu, Shen Ge, Ping Zhang, Xu sun

In addition, according to the analysis, the CA model can help existing models better attend to the abnormal regions and provide more accurate descriptions which are crucial for an interpretable diagnosis.

A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models

no code implementations NAACL 2021 Kaiyuan Liao, Yi Zhang, Xuancheng Ren, Qi Su, Xu sun, Bin He

We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions.

Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects

no code implementations NAACL 2021 Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu sun, Bin He

Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus, we propose to conduct \textit{neural network surgery} by only modifying a limited number of parameters.

Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge

no code implementations28 May 2021 Yi Zhang, Lei LI, Yunfang Wu, Qi Su, Xu sun

Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by the triples whose forms are inconsistent with the expression of language.

Learning Relation Alignment for Calibrated Cross-modal Retrieval

1 code implementation ACL 2021 Shuhuai Ren, Junyang Lin, Guangxiang Zhao, Rui Men, An Yang, Jingren Zhou, Xu sun, Hongxia Yang

To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the linguistic relation among the words and the visual relation among the regions.

Cross-Modal Retrieval

Rethinking Skip Connection with Layer Normalization in Transformers and ResNets

no code implementations15 May 2021 Fenglin Liu, Xuancheng Ren, Zhiyuan Zhang, Xu sun, Yuexian Zou

In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could be addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection.

Image Classification Machine Translation +1

Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

1 code implementation NAACL 2021 Wenkai Yang, Lei LI, Zhiyuan Zhang, Xuancheng Ren, Xu sun, Bin He

However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples.

Data Poisoning Sentiment Analysis +1

Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation

no code implementations22 Feb 2021 Shaoxiong Feng, Xuancheng Ren, Kan Li, Xu sun

The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher.

Dialogue Generation Knowledge Distillation

A Gradient-based Kernel Approach for Efficient Network Architecture Search

no code implementations1 Jan 2021 Jingjing Xu, Liang Zhao, Junyang Lin, Xu sun, Hongxia Yang

Inspired by our new finding, we explore a simple yet effective network architecture search (NAS) approach that leverages gradient correlation and gradient values to find well-performing architectures.

Image Classification Text Classification

High-Likelihood Area Matters --- Rewarding Near-Correct Predictions Under Imbalanced Distributions

no code implementations1 Jan 2021 Guangxiang Zhao, Lei LI, Xuancheng Ren, Xu sun, Bin He

We find in practice that the high-likelihood area contains correct predictions for tail classes and it plays a vital role in learning imbalanced class distributions.

CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade

1 code implementation29 Dec 2020 Lei LI, Yankai Lin, Deli Chen, Shuhuai Ren, Peng Li, Jie zhou, Xu sun

On the other hand, the exiting decisions made by internal classifiers are unreliable, leading to wrongly emitted early predictions.

Knowledge Distillation Model Selection

Distance-wise Graph Contrastive Learning

no code implementations14 Dec 2020 Deli Chen, Yanyai Lin, Lei LI, Xuancheng Ren. Peng Li, Jie zhou, Xu sun

We then propose our Distance-wise Graph Contrastive Learning (DwGCL) method from two views:(1) From the global view of the task information distribution across the graph, we enhance the CL effect on nodes that are topologically far away from labeled nodes; (2) From the personal view of each node's received information, we measure the relative distance between nodes and then we adapt the sampling strategy of GCL accordingly.

Contrastive Learning Graph Learning

EQG-RACE: Examination-Type Question Generation

1 code implementation11 Dec 2020 Xin Jia, Wenjie Zhou, Xu sun, Yunfang Wu

Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments.

Question Generation

Rethinking Skip Connection with Layer Normalization

no code implementations COLING 2020 Fenglin Liu, Xuancheng Ren, Zhiyuan Zhang, Xu sun, Yuexian Zou

In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could by addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection.

Image Classification Machine Translation +1

Prophet Attention: Predicting Attention with Future Attention

no code implementations NeurIPS 2020 Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu sun

Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.

Image Captioning

Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models

no code implementations Findings of the Association for Computational Linguistics 2020 Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu sun, Bin He

Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.

Knowledge Graph Embedding

A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment

no code implementations19 Oct 2020 Xu sun, Zhenfeng Fan, Zihao Zhang, Yingjie Guo, Shihong Xia

The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods.

Face Alignment

CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations

no code implementations13 Oct 2020 Fuli Luo, Pengcheng Yang, Shicheng Li, Xuancheng Ren, Xu sun

Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.

Natural Language Understanding

Regularizing Dialogue Generation by Imitating Implicit Scenarios

no code implementations EMNLP 2020 Shaoxiong Feng, Xuancheng Ren, Hongshen Chen, Bin Sun, Kan Li, Xu sun

Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.

Dialogue Generation Imitation Learning

Graph-based Multi-hop Reasoning for Long Text Generation

no code implementations28 Sep 2020 Liang Zhao, Jingjing Xu, Junyang Lin, Yichang Zhang, Hongxia Yang, Xu sun

The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer.

Review Generation Story Generation

Collaborative Group Learning

no code implementations16 Sep 2020 Shaoxiong Feng, Hongshen Chen, Xuancheng Ren, Zhuoye Ding, Kan Li, Xu sun

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima.

Transfer Learning

Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

1 code implementation31 Jul 2020 Xu Sun, Huihui Fang, Yehui Yang, Dongwei Zhu, Lei Wang, Junwei Liu, Yanwu Xu

In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.

Data Augmentation Retinal Vessel Segmentation

How to Ask Good Questions? Try to Leverage Paraphrases

no code implementations ACL 2020 Xin Jia, Wenjie Zhou, Xu sun, Yunfang Wu

Given a sentence and its relevant answer, how to ask good questions is a challenging task, which has many real applications.

Multi-Task Learning Paraphrase Generation +2

Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption

no code implementations10 Jun 2020 Xu Sun, Zhiyuan Zhang, Xuancheng Ren, Ruixuan Luo, Liangyou Li

We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter.

Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research

no code implementations18 May 2020 Xiangjun Peng, Zhentao Huang, Xu sun

Finally, we discuss related issues when building such a database and our future directions in the context of BROOK.

Autonomous Vehicles

Layer-Wise Cross-View Decoding for Sequence-to-Sequence Learning

no code implementations16 May 2020 Fenglin Liu, Xuancheng Ren, Guangxiang Zhao, Xu sun

In this work, we propose layer-wise cross-view decoding, where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences.

Abstractive Text Summarization Image Captioning +2

Parallel Data Augmentation for Formality Style Transfer

1 code implementation ACL 2020 Yi Zhang, Tao Ge, Xu sun

The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data.

Data Augmentation Style Transfer

Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks

2 code implementations14 Apr 2020 Shu Liu, Wei Li, Yunfang Wu, Qi Su, Xu sun

Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them.

Aspect Extraction Sentiment Analysis

Query-Variant Advertisement Text Generation with Association Knowledge

1 code implementation14 Apr 2020 Siyu Duan, Wei Li, Cai Jing, Yancheng He, Yunfang Wu, Xu sun

In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords.

Text Generation

Exploring and Distilling Cross-Modal Information for Image Captioning

no code implementations28 Feb 2020 Fenglin Liu, Xuancheng Ren, Yuanxin Liu, Kai Lei, Xu sun

Recently, attention-based encoder-decoder models have been used extensively in image captioning.

Image Captioning

Mining Commonsense Facts from the Physical World

no code implementations8 Feb 2020 Yanyan Zou, Wei Lu, Xu sun

In this paper, we propose a new task of mining commonsense facts from the raw text that describes the physical world.

Knowledge Base Completion

Visual Agreement Regularized Training for Multi-Modal Machine Translation

no code implementations27 Dec 2019 Pengcheng Yang, Boxing Chen, Pei Zhang, Xu sun

Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.

Machine Translation Translation

Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection

2 code implementations25 Dec 2019 Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu sun

Self-attention based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks.

Image Captioning Language Modelling +2

MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning

2 code implementations17 Nov 2019 Guangxiang Zhao, Xu sun, Jingjing Xu, Zhiyuan Zhang, Liangchen Luo

In this work, we explore parallel multi-scale representation learning on sequence data, striving to capture both long-range and short-range language structures.

Machine Translation Representation Learning +1

Understanding and Improving Layer Normalization

1 code implementation NeurIPS 2019 Jingjing Xu, Xu sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin

Unlike them, we find that the derivatives of the mean and variance are more important than forward normalization by re-centering and re-scaling backward gradients.

Machine Translation Translation

HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network

no code implementations10 Nov 2019 Deli Chen, Xiaoqian Liu, Yankai Lin, Peng Li, Jie zhou, Qi Su, Xu sun

To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.

General Classification Node Classification

Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification

no code implementations IJCNLP 2019 Pengcheng Yang, Junyang Lin, Jingjing Xu, Jun Xie, Qi Su, Xu sun

The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data.

Blast-wave description of $Υ$ elliptic flow at energies available at the CERN Large Hadron Collider

1 code implementation31 Oct 2019 Klaus Reygers, Alexander Schmah, Anastasia Berdnikova, Xu sun

A simultaneous blast-wave fit to particle yields and elliptic flow ($v_{2}$) measured as a function of transverse momentum in Pb-Pb collisions at LHC energies is presented.

High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory

An Adaptive and Momental Bound Method for Stochastic Learning

2 code implementations27 Oct 2019 Jianbang Ding, Xuancheng Ren, Ruixuan Luo, Xu sun

The dynamic learning rate bounds are based on the exponential moving averages of the adaptive learning rates themselves, which smooth out unexpected large learning rates and stabilize the training of deep neural networks.

Stochastic Optimization

Pun-GAN: Generative Adversarial Network for Pun Generation

1 code implementation IJCNLP 2019 Fuli Luo, Shunyao Li, Pengcheng Yang, Lei LI, Baobao Chang, Zhifang Sui, Xu sun

It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses.

Enhancing Topic-to-Essay Generation with External Commonsense Knowledge

no code implementations ACL 2019 Pengcheng Yang, Lei LI, Fuli Luo, Tianyu Liu, Xu sun

Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.

Concept-To-Text Generation

MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction

no code implementations ACL 2019 Pengcheng Yang, Fuli Luo, Peng Chen, Tianyu Liu, Xu sun

The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages.

Bilingual Lexicon Induction Denoising +2

Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information

1 code implementation ACL 2019 Pengcheng Yang, Zhihan Zhang, Fuli Luo, Lei LI, Chengyang Huang, Xu sun

Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.

Learning to Control the Fine-grained Sentiment for Story Ending Generation

no code implementations ACL 2019 Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, Xu sun

Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges.

Text Generation

A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification

1 code implementation ACL 2019 Pengcheng Yang, Fuli Luo, Shuming Ma, Junyang Lin, Xu sun

In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels.

General Classification Multi-Label Classification

Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model

1 code implementation ACL 2019 Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu sun

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.

Graph-to-Sequence

PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation

3 code implementations27 Jun 2019 Ruixuan Luo, Jingjing Xu, Yi Zhang, Xuancheng Ren, Xu sun

Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing.

Chinese Word Segmentation POS

A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer

1 code implementation ACL 2019 Chen Wu, Xuancheng Ren, Fuli Luo, Xu sun

Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision.

Style Transfer Text Style Transfer +1

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

1 code implementation4 Jun 2019 Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu sun

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.

Graph-to-Sequence

Memorized Sparse Backpropagation

no code implementations24 May 2019 Zhiyuan Zhang, Pengcheng Yang, Xuancheng Ren, Qi Su, Xu sun

Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers.

A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer

2 code implementations24 May 2019 Fuli Luo, Peng Li, Jie zhou, Pengcheng Yang, Baobao Chang, Zhifang Sui, Xu sun

Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.

Text Style Transfer Unsupervised Text Style Transfer

Adaptive Gradient Methods with Dynamic Bound of Learning Rate

5 code implementations ICLR 2019 Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu sun

Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.

Learning Personalized End-to-End Goal-Oriented Dialog

no code implementations12 Nov 2018 Liangchen Luo, Wenhao Huang, Qi Zeng, Zaiqing Nie, Xu sun

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues.

Goal-Oriented Dialog

Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy

no code implementations1 Nov 2018 Pengcheng Yang, Fuli Luo, Shuangzhi Wu, Jingjing Xu, Dong-dong Zhang, Xu sun

In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly maximizing the mean discrepancy between the distribution of transferred embedding and target embedding.

Density Estimation Machine Translation +3

Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning

no code implementations EMNLP 2018 Chen Shi, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, Lintao Zhang

The lack of labeled data is one of the main challenges when building a task-oriented dialogue system.

Active Learning

Unsupervised Machine Commenting with Neural Variational Topic Model

no code implementations13 Sep 2018 Shuming Ma, Lei Cui, Furu Wei, Xu sun

To fully exploit the unpaired data, we completely remove the need for parallel data and propose a novel unsupervised approach to train an automatic article commenting model, relying on nothing but unpaired articles and comments.

Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet

no code implementations11 Sep 2018 Shu Liu, Jingjing Xu, Xuancheng Ren, Xu sun

To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset.

Language Modelling

An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation

1 code implementation EMNLP 2018 Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, Xu sun

Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs.

Dialogue Generation

Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model

no code implementations22 Aug 2018 Deli Chen, Shuming Ma, Pengcheng Yang, Xu sun

In this work, we introduce a novel task: high-quality comment identification (HQCI), which aims to automatically assess the quality of online comments.

Text Matching

Learning Sentiment Memories for Sentiment Modification without Parallel Data

1 code implementation EMNLP 2018 Yi Zhang, Jingjing Xu, Pengcheng Yang, Xu sun

The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content.

Text Style Transfer

Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation

1 code implementation EMNLP 2018 Junyang Lin, Xu sun, Xuancheng Ren, Muyu Li, Qi Su

Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism.

Machine Translation Translation

A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation

1 code implementation EMNLP 2018 Jingjing Xu, Xuancheng Ren, Yi Zhang, Qi Zeng, Xiaoyan Cai, Xu sun

Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation.

Story Generation

Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions

no code implementations16 Aug 2018 Wei Li, Xuancheng Ren, Damai Dai, Yunfang Wu, Houfeng Wang, Xu sun

In the experiments, we take a real-world sememe knowledge base HowNet and the corresponding descriptions of the words in Baidu Wiki for training and evaluation.

Primal Meaning Recommendation via On-line Encyclopedia

no code implementations14 Aug 2018 Zhiyuan Zhang, Wei Li, Jingjing Xu, Xu sun

We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced.

A Neural Question Answering Model Based on Semi-Structured Tables

no code implementations COLING 2018 Hao Wang, Xiaodong Zhang, Shuming Ma, Xu sun, Houfeng Wang, Mengxiang Wang

Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer.

Knowledge Graphs Question Answering

SGM: Sequence Generation Model for Multi-label Classification

1 code implementation COLING 2018 Pengcheng Yang, Xu sun, Wei Li, Shuming Ma, Wei Wu, Houfeng Wang

Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.

Classification General Classification +1

Deconvolution-Based Global Decoding for Neural Machine Translation

1 code implementation COLING 2018 Junyang Lin, Xu sun, Xuancheng Ren, Shuming Ma, Jinsong Su, Qi Su

A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order.

Machine Translation Translation

Bag-of-Words as Target for Neural Machine Translation

1 code implementation ACL 2018 Shuming Ma, Xu sun, Yizhong Wang, Junyang Lin

However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage.

Machine Translation Translation

Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network

1 code implementation ACL 2018 Pengcheng Yang, Xu sun, Wei Li, Shuming Ma

As more and more academic papers are being submitted to conferences and journals, evaluating all these papers by professionals is time-consuming and can cause inequality due to the personal factors of the reviewers.

Global Encoding for Abstractive Summarization

4 code implementations ACL 2018 Junyang Lin, Xu sun, Shuming Ma, Qi Su

To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context.

Abstractive Text Summarization

Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation

no code implementations6 Feb 2018 Junyang Lin, Shuming Ma, Qi Su, Xu sun

ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration.

Machine Translation Translation

Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method

no code implementations27 Jan 2018 Wei Li, Zheng Yang, Xu sun

Traditional Chinese Medicine (TCM) is an influential form of medical treatment in China and surrounding areas.

Building an Ellipsis-aware Chinese Dependency Treebank for Web Text

2 code implementations LREC 2018 Xuancheng Ren, Xu sun, Ji Wen, Bingzhen Wei, Weidong Zhan, Zhiyuan Zhang

Web 2. 0 has brought with it numerous user-produced data revealing one's thoughts, experiences, and knowledge, which are a great source for many tasks, such as information extraction, and knowledge base construction.

Dependency Parsing

A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction

1 code implementation LREC 2018 Yi Zhang, Xu sun

However, due to the deficiency in the abbreviation corpora, such a task is limited in current studies, especially considering general abbreviation prediction should also include those full form expressions that do not have valid abbreviations, namely the negative full forms (NFFs).

Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing

1 code implementation28 Nov 2017 Xuancheng Ren, Xu sun

In the training of transition-based dependency parsers, an oracle is used to predict a transition sequence for a sentence and its gold tree.

Chinese Dependency Parsing Dependency Parsing

Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?

1 code implementation COLING 2018 Yi Zhang, Xu sun, Shuming Ma, Yang Yang, Xuancheng Ren

In our work, we first design a new model called "high order LSTM" to predict multiple tags for the current token which contains not only the current tag but also the previous several tags.

Chunking NER

A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text

2 code implementations19 Nov 2017 Jingjing Xu, Ji Wen, Xu sun, Qi Su

To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method.

Named Entity Recognition NER +1

Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

3 code implementations17 Nov 2017 Xu Sun, Xuancheng Ren, Shuming Ma, Bingzhen Wei, Wei Li, Jingjing Xu, Houfeng Wang, Yi Zhang

Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications.

Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning

no code implementations4 Nov 2017 Jingjing Xu, Xu sun, Sujian Li, Xiaoyan Cai, Bingzhen Wei

In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains.

Chinese Word Segmentation Transfer Learning

Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure

no code implementations IJCNLP 2017 Shen Huang, Xu sun, Houfeng Wang

Boundary features are widely used in traditional Chinese Word Segmentation (CWS) methods as they can utilize unlabeled data to help improve the Out-of-Vocabulary (OOV) word recognition performance.

Chinese Word Segmentation Domain Adaptation +1

Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks

1 code implementation ICLR 2018 Xu Sun, Bingzhen Wei, Xuancheng Ren, Shuming Ma

We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks.

A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification

1 code implementation6 Oct 2017 Shuming Ma, Xu sun

In this work, our goal is to improve semantic relevance between source texts and simplified texts for text summarization and text simplification.

Semantic Similarity Semantic Textual Similarity +3

Minimal Effort Back Propagation for Convolutional Neural Networks

no code implementations18 Sep 2017 Bingzhen Wei, Xu sun, Xuancheng Ren, Jingjing Xu

As traditional neural network consumes a significant amount of computing resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet effective technique to alleviate this problem.

meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting

2 code implementations ICML 2017 Xu Sun, Xuancheng Ren, Shuming Ma, Houfeng Wang

In back propagation, only a small subset of the full gradient is computed to update the model parameters.

A Generic Online Parallel Learning Framework for Large Margin Models

no code implementations2 Mar 2017 Shuming Ma, Xu sun

To speed up the training process, many existing systems use parallel technology for online learning algorithms.

Lock-Free Parallel Perceptron for Graph-based Dependency Parsing

no code implementations2 Mar 2017 Xu Sun, Shuming Ma

To deal with this problem, we propose a parallel algorithm called parallel perceptron.

Dependency Parsing

Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network

no code implementations15 Feb 2017 Jingjing Xu, Xu sun

First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model.

Chinese Word Segmentation Transfer Learning

Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features

no code implementations COLING 2016 Xu Sun

Existing asynchronous parallel learning methods are only for the sparse feature models, and they face new challenges for the dense feature models like neural networks (e. g., LSTM, RNN).

Low-Rank Matrix Completion

A New Recurrent Neural CRF for Learning Non-linear Edge Features

no code implementations14 Nov 2016 Shuming Ma, Xu sun

Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction.

Chinese Word Segmentation Chunking +2

Towards Easier and Faster Sequence Labeling for Natural Language Processing: A Search-based Probabilistic Online Learning Framework (SAPO)

4 code implementations29 Mar 2015 Xu Sun, Shuming Ma, Yi Zhang, Xuancheng Ren

We show that this method with fast training and theoretical guarantee of convergence, which is easy to implement, can support search-based optimization and obtain top accuracy.

Structure Regularization for Structured Prediction

no code implementations NeurIPS 2014 Xu Sun

Many existing systems on structured prediction focus on increasing the level of structural dependencies within the model.

Structured Prediction

Structure Regularization for Structured Prediction: Theories and Experiments

no code implementations23 Nov 2014 Xu Sun

Many existing systems on structured prediction focus on increasing the level of structural dependencies within the model.

Structured Prediction

Exact Decoding on Latent Variable Conditional Models is NP-Hard

no code implementations18 Jun 2014 Xu Sun

The computational complexity of the exact decoding/inference in latent conditional random fields is unclear.

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