Search Results for author: Xue-Qi Cheng

Found 57 papers, 23 papers with code

Continual Domain Adaptation for Machine Reading Comprehension

no code implementations25 Aug 2020 Lixin Su, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

To tackle such a challenge, in this work, we introduce the \textit{Continual Domain Adaptation} (CDA) task for MRC.

Continual Learning Domain Adaptation +3

Query Understanding via Intent Description Generation

1 code implementation25 Aug 2020 Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query.

Information Retrieval

Ranking Enhanced Dialogue Generation

no code implementations13 Aug 2020 Changying Hao, Liang Pang, Yanyan Lan, Fei Sun, Jiafeng Guo, Xue-Qi Cheng

To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper.

Dialogue Generation Response Generation

Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning

1 code implementation27 Jul 2020 Bingbing Xu, Hua-Wei Shen, Qi Cao, Keting Cen, Xue-Qi Cheng

Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data.

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

no code implementations27 Jul 2020 Bingbing Xu, Jun-Jie Huang, Liang Hou, Hua-Wei Shen, Jinhua Gao, Xue-Qi Cheng

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node.

Classification General Classification +2

Adversarial Immunization for Certifiable Robustness on Graphs

2 code implementations19 Jul 2020 Shuchang Tao, Hua-Wei Shen, Qi Cao, Liang Hou, Xue-Qi Cheng

Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.

Adversarial Attack Bilevel Optimization +2

On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation

no code implementations ICML 2020 Jianing Li, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng

We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution.

Text Generation

NeuInfer: Knowledge Inference on N-ary Facts

no code implementations ACL 2020 Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xue-Qi Cheng

It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s).

Match$^2$: A Matching over Matching Model for Similar Question Identification

no code implementations21 Jun 2020 Zizhen Wang, Yixing Fan, Jiafeng Guo, Liu Yang, Ruqing Zhang, Yanyan Lan, Xue-Qi Cheng, Hui Jiang, Xiaozhao Wang

However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i. e., there could be different ways to ask a same question or different questions sharing similar expressions.

Community Question Answering

L2R2: Leveraging Ranking for Abductive Reasoning

1 code implementation22 May 2020 Yunchang Zhu, Liang Pang, Yanyan Lan, Xue-Qi Cheng

To fill this gap, we switch to a ranking perspective that sorts the hypotheses in order of their plausibilities.

Language Modelling Learning-To-Rank +1

A Non-negative Symmetric Encoder-Decoder Approach for Community Detection

1 code implementation CIKM 2019 Bing-Jie Sun, Hua-Wei Shen, Jinhua Gao, Wentao Ouyang, Xue-Qi Cheng

Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.

Community Detection Graph Clustering +2

SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

2 code implementations12 Dec 2019 Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xue-Qi Cheng, Ji-Rong Wen

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.

Information Retrieval Learning-To-Rank

Parameter Estimation with the Ordered $\ell_{2}$ Regularization via an Alternating Direction Method of Multipliers

no code implementations4 Sep 2019 Mahammad Humayoo, Xue-Qi Cheng

The reason stems from the fact that the ordered regularization can reject irrelevant variables and yield an accurate estimation of the parameters.

Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile

no code implementations9 Jul 2019 Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications.

Language Modelling

Signed Graph Attention Networks

1 code implementation26 Jun 2019 Junjie Huang, Hua-Wei Shen, Liang Hou, Xue-Qi Cheng

We evaluate the proposed SiGAT method by applying it to the signed link prediction task.

Graph Attention Link Prediction +2

Popularity Prediction on Social Platforms with Coupled Graph Neural Networks

1 code implementation21 Jun 2019 Qi Cao, Hua-Wei Shen, Jinhua Gao, Bingzheng Wei, Xue-Qi Cheng

In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction.

ANAE: Learning Node Context Representation for Attributed Network Embedding

no code implementations20 Jun 2019 Keting Cen, Hua-Wei Shen, Jinhua Gao, Qi Cao, Bingbing Xu, Xue-Qi Cheng

In this paper, we address attributed network embedding from a novel perspective, i. e., learning node context representation for each node via modeling its attributed local subgraph.

General Classification Link Prediction +2

Soft Contextual Data Augmentation for Neural Machine Translation

1 code implementation ACL 2019 Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xue-Qi Cheng, Tie-Yan Liu

While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited.

Data Augmentation Language Modelling +2

Outline Generation: Understanding the Inherent Content Structure of Documents

no code implementations24 May 2019 Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

To generate a sound outline, an ideal OG model should be able to capture three levels of coherence, namely the coherence between context paragraphs, that between a section and its heading, and that between context headings.

Structured Prediction

MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching

1 code implementation24 May 2019 Jiafeng Guo, Yixing Fan, Xiang Ji, Xue-Qi Cheng

Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation.

Information Retrieval Natural Language Processing +2

Controlling Risk of Web Question Answering

no code implementations24 May 2019 Lixin Su, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xue-Qi Cheng

Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need.

Machine Reading Comprehension Question Answering

Graph Wavelet Neural Network

1 code implementation ICLR 2019 Bingbing Xu, Hua-Wei Shen, Qi Cao, Yunqi Qiu, Xue-Qi Cheng

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.

General Classification

Relative Importance Sampling For Off-Policy Actor-Critic in Deep Reinforcement Learning

no code implementations30 Oct 2018 Mahammad Humayoo, Xue-Qi Cheng

One reason for the instability of off-policy learning is a discrepancy between the target ($\pi$) and behavior (b) policy distributions.

reinforcement-learning

Exploiting Contextual Information via Dynamic Memory Network for Event Detection

1 code implementation EMNLP 2018 Shaobo Liu, Rui Cheng, Xiaoming Yu, Xue-Qi Cheng

Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks.

Event Detection

Tailored Sequence to Sequence Models to Different Conversation Scenarios

no code implementations ACL 2018 Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

In this paper, we propose two tailored optimization criteria for Seq2Seq to different conversation scenarios, i. e., the maximum generated likelihood for specific-requirement scenario, and the conditional value-at-risk for diverse-requirement scenario.

Dialogue Generation Response Generation

Efficient Sequence Learning with Group Recurrent Networks

no code implementations NAACL 2018 Fei Gao, Lijun Wu, Li Zhao, Tao Qin, Xue-Qi Cheng, Tie-Yan Liu

Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation, speech recognition and so on.

Machine Translation speech-recognition +2

Modeling Diverse Relevance Patterns in Ad-hoc Retrieval

2 code implementations SIGIR '18 2018 Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, ChengXiang Zhai, Xue-Qi Cheng

The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document.

A Tree Search Algorithm for Sequence Labeling

1 code implementation29 Apr 2018 Yadi Lao, Jun Xu, Yanyan Lan, Jiafeng Guo, Sheng Gao, Xue-Qi Cheng

Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word.

Chunking Decision Making +2

MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server

no code implementations22 Apr 2018 Guoxin Cui, Jun Xu, Wei Zeng, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng

One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchange the model gradients between the workers and servers during the training iterations.

BIG-bench Machine Learning Quantization +1

Locally Smoothed Neural Networks

1 code implementation22 Nov 2017 Liang Pang, Yanyan Lan, Jun Xu, Jiafeng Guo, Xue-Qi Cheng

The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields.

Face Verification Question Answering +1

Path-Based Attention Neural Model for Fine-Grained Entity Typing

no code implementations29 Oct 2017 Denghui Zhang, Pengshan Cai, Yantao Jia, Manling Li, Yuanzhuo Wang, Xue-Qi Cheng

Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure.

Entity Typing

A Deep Investigation of Deep IR Models

no code implementations24 Jul 2017 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Therefore, it is necessary to identify the difference between automatically learned features by deep IR models and hand-crafted features used in traditional learning to rank approaches.

Information Retrieval Learning-To-Rank

MatchZoo: A Toolkit for Deep Text Matching

1 code implementation23 Jul 2017 Yixing Fan, Liang Pang, Jianpeng Hou, Jiafeng Guo, Yanyan Lan, Xue-Qi Cheng

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods.

Ad-Hoc Information Retrieval Information Retrieval +2

Spherical Paragraph Model

no code implementations18 Jul 2017 Ruqing Zhang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xue-Qi Cheng

Representing texts as fixed-length vectors is central to many language processing tasks.

Natural Language Processing Representation Learning +1

Efficient Parallel Translating Embedding For Knowledge Graphs

1 code implementation30 Mar 2017 Denghui Zhang, Manling Li, Yantao Jia, Yuanzhuo Wang, Xue-Qi Cheng

Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces.

Knowledge Graph Embedding Knowledge Graphs +2

Marked Temporal Dynamics Modeling based on Recurrent Neural Network

no code implementations14 Jan 2017 Yongqing Wang, Shenghua Liu, Hua-Wei Shen, Xue-Qi Cheng

Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them.

A Study of MatchPyramid Models on Ad-hoc Retrieval

1 code implementation15 Jun 2016 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it.

Machine Translation Paraphrase Identification +3

Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

1 code implementation15 Apr 2016 Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, Xue-Qi Cheng

In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i. e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position.

Semantic Regularities in Document Representations

no code implementations24 Mar 2016 Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, Xue-Qi Cheng

Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language.

Text Matching as Image Recognition

7 code implementations20 Feb 2016 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, Xue-Qi Cheng

An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.

Ad-Hoc Information Retrieval Natural Language Processing +1

Locally Adaptive Translation for Knowledge Graph Embedding

no code implementations4 Dec 2015 Yantao Jia, Yuanzhuo Wang, Hailun Lin, Xiaolong Jin, Xue-Qi Cheng

Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space.

Knowledge Graph Embedding Knowledge Graphs +1

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

1 code implementation26 Nov 2015 Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, Xue-Qi Cheng

Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.

Information Retrieval Question Answering +1

IMRank: Influence Maximization via Finding Self-Consistent Ranking

no code implementations17 Feb 2014 Suqi Cheng, Hua-Wei Shen, Junming Huang, Wei Chen, Xue-Qi Cheng

Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2)Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.

Social and Information Networks Data Structures and Algorithms F.2.2; D.2.8

Stochastic Rank Aggregation

no code implementations26 Sep 2013 Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng

Traditional rank aggregation methods are deterministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized.

StaticGreedy: solving the scalability-accuracy dilemma in influence maximization

no code implementations19 Dec 2012 Suqi Cheng, Hua-Wei Shen, Junming Huang, Guoqing Zhang, Xue-Qi Cheng

We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization.

Social and Information Networks Data Structures and Algorithms Physics and Society F.2.2; D.2.8

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