Search Results for author: Wenjie Li

Found 77 papers, 17 papers with code

Effect Generation Based on Causal Reasoning

no code implementations Findings (EMNLP) 2021 Feiteng Mu, Wenjie Li, Zhipeng Xie

Given an input cause sentence, a causal subgraph is retrieved from the event causality network and is encoded with the graph attention mechanism, in order to support better reasoning of the potential effects.

Graph Attention

MMCoQA: Conversational Question Answering over Text, Tables, and Images

1 code implementation ACL 2022 Yongqi Li, Wenjie Li, Liqiang Nie

In this paper, we hence define a novel research task, i. e., multimodal conversational question answering (MMCoQA), aiming to answer users’ questions with multimodal knowledge sources via multi-turn conversations.

Conversational Question Answering

Event Graph based Sentence Fusion

no code implementations EMNLP 2021 Ruifeng Yuan, Zili Wang, Wenjie Li

Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence.

Abstractive Text Summarization Sentence Fusion +1

Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfer

no code implementations31 May 2022 Wenjie Li, Qiaolin Xia, Junfeng Deng, Hao Cheng, Jiangming Liu, Kouying Xue, Yong Cheng, Shu-Tao Xia

As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher.

Federated Learning Knowledge Distillation +1

Federated X-Armed Bandit

no code implementations30 May 2022 Wenjie Li, Qifan Song, Jean Honorio, Guang Lin

This work establishes the first framework of federated $\mathcal{X}$-armed bandit, where different clients face heterogeneous local objective functions defined on the same domain and are required to collaboratively figure out the global optimum.

A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method

no code implementations23 May 2022 Wei Wang, Xin Zhang, Jiaqi Yi, Xianqi Liao, Wenjie Li, Zhenhong Li

The experimental results show that the image denoising method of ZrO2 ceramic bearing ball surface defect based on cartoon-texture decomposition model can denoise while retaining the image details.

Defect Detection Image Denoising +1

Federated Online Sparse Decision Making

no code implementations27 Feb 2022 Chi-Hua Wang, Wenjie Li, Guang Cheng, Guang Lin

This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters.

Decision Making Multi-Armed Bandits

Deep Dirichlet Process Mixture Models

no code implementations29 Sep 2021 Naiqi Li, Wenjie Li, Yong Jiang, Shu-Tao Xia

In this paper we propose the deep Dirichlet process mixture (DDPM) model, which is an unsupervised method that simultaneously performs clustering and feature learning.

Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization

1 code implementation26 Aug 2021 Shichao Sun, Wenjie Li

During the training stage, with teacher forcing these models are optimized to maximize the likelihood of the gold summary given the gold summary tokens as input to the decoder, while at inference the given tokens are replaced by the generated tokens.

Abstractive Text Summarization Contrastive Learning

PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)

no code implementations SEMEVAL 2021 Rong Xiang, Jinghang Gu, Emmanuele Chersoni, Wenjie Li, Qin Lu, Chu-Ren Huang

In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context.

Lexical Complexity Prediction Word Embeddings

Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization

no code implementations17 Jun 2021 Wenjie Li, Chi-Hua Wang, Qifan Song, Guang Cheng

In this paper, we make the key delineation on the roles of resolution and statistical uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more general analysis and a more efficient algorithm design.

Data Distillation for Text Classification

no code implementations17 Apr 2021 Yongqi Li, Wenjie Li

In this paper, we study a related but orthogonal issue, data distillation, which aims to distill the knowledge from a large training dataset down to a smaller and synthetic one.

Classification General Classification +1

A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering

no code implementations17 Apr 2021 Yongqi Li, Wenjie Li, Liqiang Nie

Moreover, in order to collect more complementary information in the historical context, we also propose to incorporate the multi-round relevance feedback technique to explore the impact of the retrieval context on current question understanding.

Conversational Question Answering Open-Domain Question Answering

A Simple Unified Framework for High Dimensional Bandit Problems

no code implementations18 Feb 2021 Wenjie Li, Adarsh Barik, Jean Honorio

Stochastic high dimensional bandit problems with low dimensional structures are useful in different applications such as online advertising and drug discovery.

Drug Discovery

Incremental Knowledge Based Question Answering

no code implementations18 Jan 2021 Yongqi Li, Wenjie Li, Liqiang Nie

In the past years, Knowledge-Based Question Answering (KBQA), which aims to answer natural language questions using facts in a knowledge base, has been well developed.

Incremental Learning Knowledge Distillation +1

On the Marginal Regret Bound Minimization of Adaptive Methods

no code implementations1 Jan 2021 Wenjie Li, Guang Cheng

Numerous adaptive algorithms such as AMSGrad and Radam have been proposed and applied to deep learning recently.

Variance Reduction on General Adaptive Stochastic Mirror Descent

no code implementations26 Dec 2020 Wenjie Li, Zhanyu Wang, Yichen Zhang, Guang Cheng

In this work, we investigate the idea of variance reduction by studying its properties with general adaptive mirror descent algorithms in nonsmooth nonconvex finite-sum optimization problems.

Stochastic Deep Gaussian Processes over Graphs

1 code implementation NeurIPS 2020 Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia

In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains.

Gaussian Processes Variational Inference

Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT

1 code implementation COLING 2020 Ruifeng Yuan, Zili Wang, Wenjie Li

We also introduce a hierarchical structure, which incorporates the multi-level of granularities of the textual information into the model.

Extractive Summarization Natural Language Understanding

Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech

no code implementations19 May 2020 Wenjie Li, Benlai Tang, Xiang Yin, Yushi Zhao, Wei Li, Kang Wang, Hao Huang, Yuxuan Wang, Zejun Ma

Accent conversion (AC) transforms a non-native speaker's accent into a native accent while maintaining the speaker's voice timbre.

Increased-confidence adversarial examples for deep learning counter-forensics

no code implementations12 May 2020 Wenjie Li, Benedetta Tondi, Rongrong Ni, Mauro Barni

Transferability of adversarial examples is a key issue to apply this kind of attacks against multimedia forensics (MMF) techniques based on Deep Learning (DL) in a real-life setting.

Image Forensics

AdaX: Adaptive Gradient Descent with Exponential Long Term Memory

1 code implementation21 Apr 2020 Wenjie Li, Zhaoyang Zhang, Xinjiang Wang, Ping Luo

Although adaptive optimization algorithms such as Adam show fast convergence in many machine learning tasks, this paper identifies a problem of Adam by analyzing its performance in a simple non-convex synthetic problem, showing that Adam's fast convergence would possibly lead the algorithm to local minimums.

Computer Vision Natural Language Processing

How Does BN Increase Collapsed Neural Network Filters?

no code implementations30 Jan 2020 Sheng Zhou, Xinjiang Wang, Ping Luo, Litong Feng, Wenjie Li, Wei zhang

This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result.

object-detection Object Detection

Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization

no code implementations ACL 2019 Hai Ye, Wenjie Li, Lu Wang

Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs.

Code Generation Dialogue Management +1

Knowledge Graph Convolutional Networks for Recommender Systems

8 code implementations18 Mar 2019 Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information.

Click-Through Rate Prediction Collaborative Filtering +2

When Collaborative Filtering Meets Reinforcement Learning

no code implementations2 Feb 2019 Yu Lei, Wenjie Li

In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations.

Collaborative Filtering reinforcement-learning

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

4 code implementations23 Jan 2019 Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.

Collaborative Filtering Knowledge Graph Embedding +4

Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks

no code implementations19 Nov 2018 Chenchen Li, Jialin Wang, Hongwei Wang, Miao Zhao, Wenjie Li, Xiaotie Deng

To enhance the emotion discriminativeness of words in textual feature extraction, we propose Emotional Word Embedding (EWE) to learn text representations by jointly considering their semantics and emotions.

Emotion Recognition MULTI-VIEW LEARNING

Incorporating Relevant Knowledge in Context Modeling and Response Generation

no code implementations9 Nov 2018 Yan-ran Li, Wenjie Li, Ziqiang Cao, Chengyao Chen

To sustain engaging conversation, it is critical for chatbots to make good use of relevant knowledge.

Chatbot Response Generation

Meta-path Augmented Response Generation

no code implementations2 Nov 2018 Yan-ran Li, Wenjie Li

We propose a chatbot, namely Mocha to make good use of relevant entities when generating responses.

Chatbot Response Generation

Variational Autoregressive Decoder for Neural Response Generation

no code implementations EMNLP 2018 Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation.

Response Generation

NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation

no code implementations EMNLP 2018 Hui Su, Xiaoyu Shen, Wenjie Li, Dietrich Klakow

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems.

Dialogue Generation

Large scale classification in deep neural network with Label Mapping

no code implementations7 Jun 2018 Qizhi Zhang, Kuang-Chih Lee, Hongying Bao, Yuan You, Wenjie Li, Dongbai Guo

Therefore, it is infeasible to solve the multi-class classification problem using deep neural network when the number of classes are huge.

Classification General Classification +1

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

10 code implementations9 Mar 2018 Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.

Click-Through Rate Prediction Collaborative Filtering +2

Faithful to the Original: Fact Aware Neural Abstractive Summarization

no code implementations13 Nov 2017 Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li

While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system.

Abstractive Text Summarization Extractive Summarization +3

DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

13 code implementations IJCNLP 2017 Yan-ran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, Shuzi Niu

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.

Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

no code implementations26 Feb 2017 Tong Che, Yan-ran Li, Ruixiang Zhang, R. Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio

Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted.

Mode Regularized Generative Adversarial Networks

no code implementations7 Dec 2016 Tong Che, Yan-ran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes.

Content-based Influence Modeling for Opinion Behavior Prediction

no code implementations COLING 2016 Chengyao Chen, Zhitao Wang, Yu Lei, Wenjie Li

The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors.

Joint Copying and Restricted Generation for Paraphrase

no code implementations28 Nov 2016 Ziqiang Cao, Chuwei Luo, Wenjie Li, Sujian Li

In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.

Abstractive Text Summarization Informativeness +2

Improving Multi-Document Summarization via Text Classification

no code implementations28 Nov 2016 Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei

Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents.

Classification Document Summarization +3

Emotion Corpus Construction Based on Selection from Hashtags

no code implementations LREC 2016 Minglei Li, Yunfei Long, Lu Qin, Wenjie Li

Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels.

Emotion Classification

AttSum: Joint Learning of Focusing and Summarization with Neural Attention

no code implementations COLING 2016 Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, Yan-ran Li

Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.

Component-Enhanced Chinese Character Embeddings

no code implementations EMNLP 2015 Yan-ran Li, Wenjie Li, Fei Sun, Sujian Li

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English.

General Classification Text Classification +2

A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines

no code implementations5 Feb 2015 Xiaozhao Zhao, Yuexian Hou, Dawei Song, Wenjie Li

We then revisit Boltzmann machines (BM) from a model selection perspective and theoretically show that both the fully visible BM (VBM) and the BM with hidden units can be derived from the general binary multivariate distribution using the CIF principle.

Density Estimation Dimensionality Reduction +1

Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle

no code implementations16 Feb 2013 Xiaozhao Zhao, Yuexian Hou, Qian Yu, Dawei Song, Wenjie Li

Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data.

Density Estimation Dimensionality Reduction

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