Search Results for author: Lu Wang

Found 85 papers, 15 papers with code

Modeling Content Importance for Summarization with Pre-trained Language Models

no code implementations EMNLP 2020 Liqiang Xiao, Lu Wang, Hao He, Yaohui Jin

Previous work is mostly based on statistical methods that estimate word-level salience, which does not consider semantics and larger context when quantifying importance.

Temporal Logic Point Processes

no code implementations ICML 2020 Shuang Li, Lu Wang, Ruizhi Zhang, xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song

We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.

Point Processes

Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals

no code implementations31 May 2022 Marcel Robitaille, HeeBong Yang, Lu Wang, Na Young Kim

Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements.

Transfer Learning

Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs

1 code implementation NAACL 2022 Xu Wang, Simin Fan, Jessica Houghton, Lu Wang

NLP-powered automatic question generation (QG) techniques carry great pedagogical potential of saving educators' time and benefiting student learning.

Misconceptions Question Generation

Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model

no code implementations7 Apr 2022 Nick J. C. Wang, Lu Wang, Yandan Sun, Haimei Kang, Dejun Zhang

We revisit ideas presented by Lugosch et al. using speech pre-training and three-module modeling; however, to ease construction of the end-to-end SLU model, we use as our phoneme module an open-source acoustic-phonetic model from a DNN-HMM hybrid automatic speech recognition (ASR) system instead of training one from scratch.

Automatic Speech Recognition Intent Classification +2

Efficient Argument Structure Extraction with Transfer Learning and Active Learning

no code implementations Findings (ACL) 2022 Xinyu Hua, Lu Wang

Combined with transfer learning, substantial F1 score boost (5-25) can be further achieved during the early iterations of active learning across domains.

Active Learning Transfer Learning

HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization

no code implementations ACL 2022 Shuyang Cao, Lu Wang

In this work, we present HIBRIDS, which injects Hierarchical Biases foR Incorporating Document Structure into the calculation of attention scores.

Document Summarization

Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search

no code implementations9 Feb 2022 Lu Wang, Jie Yang, Masoumeh Zareapoor, ZhongLong Zheng

Cross-modal hashing still has some challenges needed to address: (1) most existing CMH methods take graphs as input to model data distribution.

Auto robust relative radiometric normalization via latent change noise modelling

no code implementations24 Nov 2021 Shiqi Liu, Lu Wang, Jie Lian, Ting Chen, Cong Liu, Xuchen Zhan, Jintao Lu, Jie Liu, Ting Wang, Dong Geng, Hongwei Duan, Yuze Tian

Relative radiometric normalization(RRN) of different satellite images of the same terrain is necessary for change detection, object classification/segmentation, and map-making tasks.

Change Detection

Rapid Assessments of Light-Duty Gasoline Vehicle Emissions Using On-Road Remote Sensing and Machine Learning

no code implementations1 Oct 2021 Yan Xia, Linhui Jiang, Lu Wang, Xue Chen, Jianjie Ye, Tangyan Hou, Liqiang Wang, Yibo Zhang, Mengying Li, Zhen Li, Zhe Song, Yaping Jiang, Weiping Liu, Pengfei Li, Daniel Rosenfeld, John H. Seinfeld, Shaocai Yu

Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions.

Controllable Summarization with Constrained Markov Decision Process

1 code implementation7 Aug 2021 Hou Pong Chan, Lu Wang, Irwin King

We study controllable text summarization which allows users to gain control on a particular attribute (e. g., length limit) of the generated summaries.

Text Summarization

Controllable Open-ended Question Generation with A New Question Type Ontology

1 code implementation ACL 2021 Shuyang Cao, Lu Wang

We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words.

Question Generation

An Efficient Group-based Search Engine Marketing System for E-Commerce

no code implementations24 Jun 2021 Cheng Jie, Da Xu, Zigeng Wang, Lu Wang, Wei Shen

With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies.


Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View

no code implementations24 Jun 2021 Shuang Li, Lu Wang, Xinyun Chen, Yixiang Fang, Yan Song

In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease.

Imitation Learning Point Processes

Learning To Segment Actions From Visual and Language Instructions via Differentiable Weak Sequence Alignment

no code implementations CVPR 2021 YuHan Shen, Lu Wang, Ehsan Elhamifar

We address the problem of unsupervised localization of key-steps and feature learning in instructional videos using both visual and language instructions.

LoRA: Low-Rank Adaptation of Large Language Models

3 code implementations ICLR 2022 Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen

We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.

Language Modelling Natural Language Processing

DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation

no code implementations ACL 2021 Xinyu Hua, Ashwin Sreevatsa, Lu Wang

To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims.

Text Generation

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

no code implementations17 May 2021 Lu Wang, xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei zhang, Xiaofeng He, Le Song, Jingren Zhou, Hongxia Yang

Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.

Contrastive Learning Graph Learning +2

Unsupervised Learning of Multi-level Structures for Anomaly Detection

no code implementations25 Apr 2021 Songmin Dai, Jide Li, Lu Wang, Congcong Zhu, Yifan Wu, Xiaoqiang Li

This paper first introduces a novel method to generate anomalous data by breaking up global structures while preserving local structures of normal data at multiple levels.

Anomaly Detection

Attention Head Masking for Inference Time Content Selection in Abstractive Summarization

no code implementations NAACL 2021 Shuyang Cao, Lu Wang

Using attention head masking, we are able to reveal the relation between encoder-decoder attentions and content selection behaviors of summarization models.

Abstractive Text Summarization Document Summarization

Inference Time Style Control for Summarization

no code implementations NAACL 2021 Shuyang Cao, Lu Wang

How to generate summaries of different styles without requiring corpora in the target styles, or training separate models?

Efficient Attentions for Long Document Summarization

1 code implementation NAACL 2021 Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, Lu Wang

The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization.

Document Summarization

MD-MTL: An Ensemble Med-Multi-Task Learning Package for DiseaseScores Prediction and Multi-Level Risk Factor Analysis

no code implementations5 Mar 2021 Lu Wang, Haoyan Jiang, Mark Chignell

In this paper, we developed a new ensemble machine learning Python package based on multi-task learning (MTL), referred to as the Med-Multi-Task Learning (MD-MTL) package and applied it in predicting disease scores of patients, and in carrying out risk factor analysis on multiple subgroups of patients simultaneously.

BIG-bench Machine Learning Multi-Task Learning

Annotation-Efficient Learning for Medical Image Segmentation based on Noisy Pseudo Labels and Adversarial Learning

no code implementations29 Dec 2020 Lu Wang, Dong Guo, Guotai Wang, Shaoting Zhang

In this paper, we propose an annotation-efficient learning framework for segmentation tasks that avoids annotations of training images, where we use an improved Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of unpaired medical images and auxiliary masks obtained either from a shape model or public datasets.

Medical Image Segmentation Semantic Segmentation

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

no code implementations25 Oct 2020 Wen Sun, Shiyu Lei, Lu Wang, Zhiqiang Liu, Yan Zhang

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4. 0 benefits.

Federated Learning

PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation

no code implementations EMNLP 2020 Xinyu Hua, Lu Wang

In this work, we present a novel content-controlled text generation framework, PAIR, with planning and iterative refinement, which is built upon a large model, BART.

Text Generation

Dynamic Online Conversation Recommendation

no code implementations ACL 2020 Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, Kam-Fai Wong

Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner.

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM

no code implementations24 Jun 2020 Yong Chen, Lu Wang, Jiajia Hu, Mingbin Ye

Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years.

Event Detection

XREF: Entity Linking for Chinese News Comments with Supplementary Article Reference

no code implementations AKBC 2020 Xinyu Hua, Lei LI, Lifeng Hua, Lu Wang

We therefore propose a novel model, XREF, that leverages attention mechanisms to (1) pinpoint relevant context within comments, and (2) detect supporting entities from the news article.

Entity Linking

Provably Robust Metric Learning

2 code implementations NeurIPS 2020 Lu Wang, Xuanqing Liu, Jin-Feng Yi, Yuan Jiang, Cho-Jui Hsieh

Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied.

Metric Learning

Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data

1 code implementation11 May 2020 Lu Wang, huan zhang, Jin-Feng Yi, Cho-Jui Hsieh, Yuan Jiang

By constraining adversarial perturbations in a low-dimensional subspace via spanning an auxiliary unlabeled dataset, the spanning attack significantly improves the query efficiency of a wide variety of existing black-box attacks.

Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

1 code implementation ACL 2020 Luyang Huang, Lingfei Wu, Lu Wang

Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive.

Abstractive Text Summarization Cloze Test +2

Weakly-supervised 3D coronary artery reconstruction from two-view angiographic images

no code implementations26 Mar 2020 Lu Wang, Dong-Xue Liang, Xiao-Lei Yin, Jing Qiu, Zhi-Yun Yang, Jun-Hui Xing, Jian-Zeng Dong, Zhao-Yuan Ma

The reconstruction of three-dimensional models of coronary arteries is of great significance for the localization, evaluation and diagnosis of stenosis and plaque in the arteries, as well as for the assisted navigation of interventional surgery.

3D Reconstruction

Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net

no code implementations26 Mar 2020 Lu Wang, Dong-Xue Liang, Xiao-Lei Yin, Jing Qiu, Zhi-Yun Yang, Jun-Hui Xing, Jian-Zeng Dong, Zhao-Yuan Ma

This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels.

Video Segmentation Video Semantic Segmentation

Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval

no code implementations14 Jan 2020 Lu Wang, Jie Yang

Due to the superiority in similarity computation and database storage for large-scale multiple modalities data, cross-modal hashing methods have attracted extensive attention in similarity retrieval across the heterogeneous modalities.

Cross-Modal Retrieval Quantization

Cluster-wise Unsupervised Hashing for Cross-Modal Similarity Search

no code implementations11 Nov 2019 Lu Wang, Jie Yang

Large-scale cross-modal hashing similarity retrieval has attracted more and more attention in modern search applications such as search engines and autopilot, showing great superiority in computation and storage.

Neural Conversation Recommendation with Online Interaction Modeling

no code implementations IJCNLP 2019 Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis.

Collaborative Filtering

Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies

no code implementations ICLR 2020 Xinyun Chen, Lu Wang, Yizhe Hang, Heng Ge, Hongyuan Zha

We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies.

Learning Robust Representations with Graph Denoising Policy Network

no code implementations4 Oct 2019 Lu Wang, Wenchao Yu, Wei Wang, Wei Cheng, Wei zhang, Hongyuan Zha, Xiaofeng He, Haifeng Chen

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.

Denoising Graph Representation Learning +2

In Plain Sight: Media Bias Through the Lens of Factual Reporting

1 code implementation IJCNLP 2019 Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, Lu Wang

The increasing prevalence of political bias in news media calls for greater public awareness of it, as well as robust methods for its detection.

An Entity-Driven Framework for Abstractive Summarization

no code implementations IJCNLP 2019 Eva Sharma, Luyang Huang, Zhe Hu, Lu Wang

Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.

Abstractive Text Summarization Document Summarization

Sentence-Level Content Planning and Style Specification for Neural Text Generation

no code implementations IJCNLP 2019 Xinyu Hua, Lu Wang

Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems.

Text Generation

BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization

no code implementations ACL 2019 Eva Sharma, Chen Li, Lu Wang

Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure.

Text Summarization

Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective

1 code implementation10 Jun 2019 Lu Wang, Xuanqing Liu, Jin-Feng Yi, Zhi-Hua Zhou, Cho-Jui Hsieh

Furthermore, we show that dual solutions for these QP problems could give us a valid lower bound of the adversarial perturbation that can be used for formal robustness verification, giving us a nice view of attack/verification for NN models.

Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards

1 code implementation ACL 2019 Hou Pong Chan, Wang Chen, Lu Wang, Irwin King

To address this problem, we propose a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases.

Keyphrase Generation Natural Language Processing +1

Joint Effects of Context and User History for Predicting Online Conversation Re-entries

1 code implementation ACL 2019 Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

We hypothesize that both the context of the ongoing conversations and the users' previous chatting history will affect their continued interests in future engagement.

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 +2

Optimal margin Distribution Network

no code implementations ICLR 2019 Shen-Huan Lv, Lu Wang, Zhi-Hua Zhou

Recent research about margin theory has proved that maximizing the minimum margin like support vector machines does not necessarily lead to better performance, and instead, it is crucial to optimize the margin distribution.

IvaNet: Learning to jointly detect and segment objets with the help of Local Top-Down Modules

no code implementations18 Mar 2019 Shihua Huang, Lu Wang

Driven by Convolutional Neural Networks, object detection and semantic segmentation have gained significant improvements.

object-detection Object Detection +1

Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution

no code implementations ICLR 2019 Shen-Huan Lyu, Lu Wang, Zhi-Hua Zhou

We utilize a convex margin distribution loss function on the deep neural networks to validate our theoretical results by optimizing the margin ratio.

Representation Learning

Learning Segmentation Masks with the Independence Prior

no code implementations12 Nov 2018 Songmin Dai, Xiaoqiang Li, Lu Wang, Pin Wu, Weiqin Tong, Yimin Chen

We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.

Instance Segmentation Weakly-Supervised Semantic Segmentation

Hull Form Optimization with Principal Component Analysis and Deep Neural Network

no code implementations27 Oct 2018 Dongchi Yu, Lu Wang

Designing and modifying complex hull forms for optimal vessel performances have been a major challenge for naval architects.

Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information

no code implementations14 Oct 2018 Lisa Fan, Dong Yu, Lu Wang

Sequence-to-sequence (seq2seq) neural models have been actively investigated for abstractive summarization.

Abstractive Text Summarization

GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation

no code implementations27 Sep 2018 Guoshuai Zhao, Jun Li, Lu Wang, Xueming Qian, Yun Fu

In this paper, we propose a Graph-Sequence-to-Sequence(GraphSeq2Seq) model to fuse the dependency graph among words into the traditional Seq2Seq framework.

Image Captioning Machine Translation +3

Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation

no code implementations4 Jul 2018 Lu Wang, Wei zhang, Xiaofeng He, Hongyuan Zha

Prior relevant studies recommend treatments either use supervised learning (e. g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e. g. maximizing evaluation signal which indicates cumulative reward from survival rates).

Recommendation Systems reinforcement-learning

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

no code implementations NAACL 2018 Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong

We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics.

Neural Argument Generation Augmented with Externally Retrieved Evidence

no code implementations ACL 2018 Xinyu Hua, Lu Wang

High quality arguments are essential elements for human reasoning and decision-making processes.

Decision Making

Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes

no code implementations TACL 2017 Lu Wang, Nick Beauchamp, Sarah Shugars, Kechen Qin

Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%).

Joint Modeling of Content and Discourse Relations in Dialogues

no code implementations ACL 2017 Kechen Qin, Lu Wang, Joseph Kim

We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns.

Understanding and Detecting Supporting Arguments of Diverse Types

no code implementations ACL 2017 Xinyu Hua, Lu Wang

We investigate the problem of sentence-level supporting argument detection from relevant documents for user-specified claims.

Summarizing Decisions in Spoken Meetings

no code implementations25 Jun 2016 Lu Wang, Claire Cardie

This paper addresses the problem of summarizing decisions in spoken meetings: our goal is to produce a concise {\it decision abstract} for each meeting decision.

Topic Models

Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings

no code implementations WS 2012 Lu Wang, Claire Cardie

We present a token-level decision summarization framework that utilizes the latent topic structures of utterances to identify "summary-worthy" words.

Decision Making Topic Models

Focused Meeting Summarization via Unsupervised Relation Extraction

no code implementations WS 2012 Lu Wang, Claire Cardie

We present a novel unsupervised framework for focused meeting summarization that views the problem as an instance of relation extraction.

Extractive Summarization Meeting Summarization +1

Socially-Informed Timeline Generation for Complex Events

no code implementations HLT 2015 Lu Wang, Claire Cardie, Galen Marchetti

Existing timeline generation systems for complex events consider only information from traditional media, ignoring the rich social context provided by user-generated content that reveals representative public interests or insightful opinions.


A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection

no code implementations ACL 2014 Lu Wang, Claire Cardie

We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole.

Sentiment Analysis

Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon

no code implementations WS 2014 Lu Wang, Claire Cardie

For example, the isotonic CRF model achieves F1 scores of 0. 74 and 0. 67 for agreement and disagreement detection, when a linear chain CRF obtains 0. 58 and 0. 56 for the discussions on Wikipedia Talk pages.

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