Pengfei Liu

Heterogeneous Graph Neural Networks for Extractive Document Summarization

As a crucial step in extractive document summarization, learning cross-s...
04/26/2020 ∙ by Danqing Wang, et al. ∙ 0 ∙ share

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Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis

Microbial communities analysis is drawing growing attention due to the r...
04/20/2020 ∙ by Yong He, et al. ∙ 0 ∙ share

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Extractive Summarization as Text Matching

This paper creates a paradigm shift with regard to the way we build neur...
04/19/2020 ∙ by Ming Zhong, et al. ∙ 0 ∙ share

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Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study

While neural network-based models have achieved impressive performance o...
01/12/2020 ∙ by Jinlan Fu, et al. ∙ 0 ∙ share

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RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

In this work, we propose an efficient and accurate monocular 3D detectio...
01/10/2020 ∙ by Peixuan Li, et al. ∙ 14 ∙ share

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Multi-Scale Self-Attention for Text Classification

In this paper, we introduce the prior knowledge, multi-scale structure, ...
12/02/2019 ∙ by Qipeng Guo, et al. ∙ 0 ∙ share

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Learning Sparse Sharing Architectures for Multiple Tasks

Most existing deep multi-task learning models are based on parameter sha...
11/12/2019 ∙ by Tianxiang Sun, et al. ∙ 0 ∙ share

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A Closer Look at Data Bias in Neural Extractive Summarization Models

In this paper, we take stock of the current state of summarization datas...
09/30/2019 ∙ by Ming Zhong, et al. ∙ 0 ∙ share

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Exploring Domain Shift in Extractive Text Summarization

Although domain shift has been well explored in many NLP applications, i...
08/30/2019 ∙ by Danqing Wang, et al. ∙ 0 ∙ share

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Zero-shot Text-to-SQL Learning with Auxiliary Task

Recent years have seen great success in the use of neural seq2seq models...
08/29/2019 ∙ by Shuaichen Chang, et al. ∙ 0 ∙ share

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DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Variants dropout methods have been designed for the fully-connected laye...
07/25/2019 ∙ by Lin Zehui, et al. ∙ 0 ∙ share

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Searching for Effective Neural Extractive Summarization: What Works and What's Next

The recent years have seen remarkable success in the use of deep neural ...
07/08/2019 ∙ by Ming Zhong, et al. ∙ 0 ∙ share

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TIGS: An Inference Algorithm for Text Infilling with Gradient Search

Text infilling is defined as a task for filling in the missing part of a...
05/26/2019 ∙ by Dayiheng Liu, et al. ∙ 0 ∙ share

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A Combinatorial Algorithm for the Multi-commodity Flow Problem

This paper researches combinatorial algorithms for the multi-commodity f...
04/20/2019 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Star-Transformer

Although the fully-connected attention-based model Transformer has achie...
02/25/2019 ∙ by Qipeng Guo, et al. ∙ 0 ∙ share

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Drug cell line interaction prediction

Understanding the phenotypic drug response on cancer cell lines plays a ...
12/28/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Multi-task Learning over Graph Structures

We present two architectures for multi-task learning with neural sequenc...
11/26/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Contextualized Non-local Neural Networks for Sequence Learning

Recently, a large number of neural mechanisms and models have been propo...
11/21/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Meta-Learning Multi-task Communication

In this paper, we describe a general framework: Parameters Read-Write Ne...
10/23/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network

Effective representation of a text is critical for various natural langu...
08/08/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Meta Multi-Task Learning for Sequence Modeling

Semantic composition functions have been playing a pivotal role in neura...
02/25/2018 ∙ by Junkun Chen, et al. ∙ 0 ∙ share

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Dynamic Compositional Neural Networks over Tree Structure

Tree-structured neural networks have proven to be effective in learning ...
05/11/2017 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Adversarial Multi-task Learning for Text Classification

Neural network models have shown their promising opportunities for multi...
04/19/2017 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Syntax-based Attention Model for Natural Language Inference

Introducing attentional mechanism in neural network is a powerful concep...
07/22/2016 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Modelling Interaction of Sentence Pair with coupled-LSTMs

Recently, there is rising interest in modelling the interactions of two ...
05/18/2016 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Recurrent Neural Network for Text Classification with Multi-Task Learning

Neural network based methods have obtained great progress on a variety o...
05/17/2016 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Pengfei Liu

Featured Co-authors

Zheng Zhang

Jian Tang

Xiaodong He

Xipeng Qiu

Qi Zhang

Xuanjing Huang

Yiran Chen

Xiangyang Xue

Bowen Zhou

Jie Fu

Thomas L. Griffiths

Heterogeneous Graph Neural Networks for Extractive Document Summarization

Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis

Extractive Summarization as Text Matching

Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

Multi-Scale Self-Attention for Text Classification

Learning Sparse Sharing Architectures for Multiple Tasks

A Closer Look at Data Bias in Neural Extractive Summarization Models

Exploring Domain Shift in Extractive Text Summarization

Zero-shot Text-to-SQL Learning with Auxiliary Task

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Searching for Effective Neural Extractive Summarization: What Works and What's Next

TIGS: An Inference Algorithm for Text Infilling with Gradient Search

A Combinatorial Algorithm for the Multi-commodity Flow Problem

Star-Transformer

Drug cell line interaction prediction

Multi-task Learning over Graph Structures

Contextualized Non-local Neural Networks for Sequence Learning

Meta-Learning Multi-task Communication

Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network

Meta Multi-Task Learning for Sequence Modeling

Dynamic Compositional Neural Networks over Tree Structure

Adversarial Multi-task Learning for Text Classification

Syntax-based Attention Model for Natural Language Inference

Modelling Interaction of Sentence Pair with coupled-LSTMs

Recurrent Neural Network for Text Classification with Multi-Task Learning