Liang Pang

庞 亮

Associate Professor

Data Intelligence System Research Center

Institute of Computing Technology (ICT)

Chinese Academy of Sciences (CAS)

  • Email : pangliang AT ict.ac.cn
  • Office: : (8610)62600964
  • Address : NO. 6 Kexueyuan South Road, Haidian District, Beijing, P.R.China, 100190
  • Biography
  • Publication
  • ToolKits & Demos

About Me

Liang Pang is an associate professor at Institute of Computing Technology, Chinese Academy of Sciences. He received Ph.D. degree in Computer Science from University of Chinese Academy of Sciences in 2018 under supervising by Prof. Guojie Li. Before that, He get a B.S. degree in Software Engineering from Huazhong University of Science and Technology in 2012.

His current research is focused on deep learning in text matching tasks, including ranking, query answering, paraphrase identification.

Talks

KDD 2024 Tutorial

Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, Jun Xu. Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models. The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24) [Survey Paper] [slides] [videos]

SIGIR 2021 Tutorial

Liang Pang, Qingyao Ai, Jun Xu. Beyond Probability Ranking Principle: Modeling the Dependencies among Documents. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. (SIGIR 2021) [pdf] [slides] [videos]

WSDM 2021 Tutorial

Liang Pang, Qingyao Ai, Jun Xu. Beyond Probability Ranking Principle: Modeling the Dependencies among Documents. The 14th ACM International Conference on Web Search and Data Mining. (WSDM 2021) [pdf] [slides] [Part1] [Part2] [Part3]

CIPS Summer School 2018 中国中文信息学会《前沿技术讲习班》

Jun Xu, Liang Pang. Deep and Reinforcement Learning for Information Retrieval.信息检索中的深度强化学习新进展. [slides] [site]

Open Source

Dataset: KEBench, a novel benchmark specifically designed for assessing knowledge editing methods

code (https://github.com/Hi-archers/StableKE)

AISO - Adaptive Information Seeking for Open-Domain Question Answering

code (https://github.com/zycdev/AISO)

LLMDet：A Third Party Large Language Models Generated Text Detection Tool

code (https://github.com/TrustedLLM/LLMDet)

TVR - Transformation driven Visual Reasoning

code (https://github.com/hughplay/TVR)

demo (https://hongxin2019.github.io/TVR)

Project: MatchZoo - Open ToolKit for Deep Text Matching & Neural IR

MatchZoo (https://github.com/NTMC-Community/matchzoo)

Project: TextNet - Open Framework of Deep Learning for Text (C++)

TextNet

TextNet (https://github.com/pl8787/textnet-release)

Hornors and Awards

Academic Services

  • PC member for AAAI2019
  • PC member for ACML2019
  • PC member for CCIR2018
  • Reviewer for: TIST
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015 and before
MatchZoo (https://github.com/NTMC-Community/matchzoo)

MatchingZoo is a toolkit for text matching. It was developed with a focus on facilitate the designing, comparing and sharing of deep text matching models. The architecture of the MatchZoo toolit is depicited in Figure. There are three major modules in the toolkit, namely data preparation, model construction, training and evaluation, respectively. These three modules are actually organized as a pipeline of data flow.

Reference:

Yixing Fan, Liang Pang, Jianpeng Hou, Jiafeng Guo, Yanyan Lan and Xueqi Cheng. MatchZoo: A Toolkit for Deep Text Matching. Neu-IR: The SIGIR 2017 Workshop on Neural Information Retrieval, Tokyo, 2017.

Academic Benchmark (http://www.bigdatalab.ac.cn/benchmark/)

A platform for not only reporting the performances of state-of-the-art algorithms in different domains, but also providing the corresponding datasets, codes, and scripts that can make the experimental results reproducible.