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Liangming Pan

Ph.D. Student

National University of Singapore

Biography

Liangming Pan (潘亮铭) is a third year Computer Science Ph.D. student at National University of Singapore, jointly advised by Prof. Min-Yen Kan and Prof. Tat-Seng Chua. Prior to joining NUS, he received a Master degree from School of Computer Science at Tsinghua University in June, 2017, working with Prof. Juanzi Li and Prof. Jie Tang. He obtained the Bachelor degree in Beihang University (2010 - 2014).

His board research interests include knowledge base, natural language processing, and data mining. To be specific, his research topics include neural question generation, text style transfer, and zero/few-shot image recognition.

He is now joining the UC Santa Barbara Natural Language Processing Group as a visiting Ph.D. student, supervised by Prof. William Wang.

Interests

  • Text Generation
  • Knowledge Graph
  • Multi-media Learning

Education

  • PhD in Computer Science, 2018

    National University of Singapore

  • MEng in Computer Science, 2014

    Tsinghua University

  • BSc in Software Engineering, 2010

    Beihang University

News

One paper accepted by ACM MM 2020!

ACM MM 2020 Jul 2020
Our paper “Multi-modal Cooking Workflow Construction for Food Recipes” was accepted by ACM MM 2020 (acceptance rate: 27.8%). The full paper will be released soon.

Joining the UC Santa Barbara NLP Group!

UCSB NLP Group Jul 2020
From August 2020 to July 2021, I am joining the UC Santa Barbara Natural Language Processing Group as a visiting Ph.D. student, supervised by Prof. William Wang.

Two paper accepted by ACL 2020!

ACL 2020 Apr 2020
Two of our papers, “Semantic Graphs for Generating Deep Questions” and “Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymenwas”, were accepted by ACL 2020 (acceptance rate: 25.2%).

One paper accepted by CVPR 2020!

CVPR 2020 Feb 2020
Our paper “Hyperbolic Visual Embedding Learning for Zero-Shot Recognition” was accepted by CVPR 2020 (acceptance rate: 22%).

Paper list on neural question generation on Github.

Github Dec 2019
We created a paper list on Github to summarize the recent research on neural question generation. [Github link]

One paper accepted by AAAI 2020!

AAAI 2020 Nov 2019
Our paper “Zero-shot Ingredient Recognition by Multi-Relational Graph Convolutional Network” was accepted by AAAI 2020 (acceptance rate: 20.6%). [paper link]

Survey paper on neural question generation

ArXiv May 2019
We have written a survey paper on recent advances in neural question generation, and posted it on ArXiv. [paper link]

Publications

Quickly discover relevant content by filtering publications.
Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, Min-Yen Kan . Semantic Graphs for Generating Deep Questions. ACL, 2020.

PDF Code ACL, 2020

Yixin Cao, Ruihao Shui, Liangming Pan, Min-Yen Kan, Zhiyuan Liu, Tat-Seng Chua . Expertise Style Transfer: A New Task Towards Better Communcation between Experts and Laymen. ACL, 2020.

PDF Dataset ACL, 2020

Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang . Hyperbolic Visual Embedding Learning for Zero-Shot Recognition. CVPR, 2020.

PDF Code CVPR, 2020

Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua . Zero-shot Ingredient Recognition by Multi-Relational Graph Convolutional Network. AAAI, 2020.

PDF AAAI, 2020

Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yan Kan . Recent Advances in Neural Question Generation. arXiv preprint, 2019.

PDF Project

Yahui An, Liangming Pan, Min-Yen Kan, Qiang Dong, Yan Fu . Resource Mention Extraction for MOOC Discussion Forums. IEEE Access 7 (2019): 87887-87900, 2019.

PDF

Jifan Yu, Liangming Pan, Juanzi Li, Xiaoping Du . Predicting Concept-based Research Trends with Rhetorical Framing. CCKS, 2018.

PDF

Liangming Pan, Xiaochen Wang, Chengjiang Li, Juanzi Li, Jie Tang . Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation. IJCNLP, 2017.

PDF Slides IJCNLP, 2017

Liangming Pan, Chengjiang Li, Juanzi Li, Jie Tang . Prerequisite Relation Learning for Concepts in MOOCs. ACL, 2017.

PDF Slides ACL, 2017

Liangming Pan, Zhigang Wang, Juanzi Li, Jie Tang . Domain Specific Cross-Lingual Knowledge Linking Based on Similarity Flooding. KSEM, 2016.

PDF Slides

See all publications

Featured Research

Semantic Graphs for Generating Deep Questions

This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over …
PDF Code ACL, 2020

Expertise Style Transfer: A New Task Towards Better Communcation between Experts and Laymen

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and …
PDF Dataset ACL, 2020

Hyperbolic Visual Embedding Learning for Zero-Shot Recognition

This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in …
PDF Code CVPR, 2020

Zero-shot Ingredient Recognition by Multi-Relational Graph Convolutional Network

Recognizing ingredients for a given dish image is at the core of automatic dietary assessment, attracting increasing attention from …
PDF AAAI, 2020

Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation

Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education. One challenging issue in MOOCs …
PDF Slides IJCNLP, 2017

Prerequisite Relation Learning for Concepts in MOOCs

What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study …
PDF Slides ACL, 2017

Projects

SG-Deep Question Generation Star

This repository contains code and models for the paper: Semantic Graphs for Generating Deep Questions (ACL 2020).

Question Generation Paper List Star

A paper list that summarizes the recent papers on neural question generation.