Yong Jiang

Alibaba DAMO Academy

Biography

Hi! I currently work at Alibaba DAMO Academy. I received my Ph.D from the joint program of ShanghaiTech University and University of Chinese Academy of Sciences. I was very fortunate to be advised by Prof. Kewei Tu. I am interested in machine learning and natural language processing.

My current research mainly focuses on multilingual NLP. Examples are here. In my PhD time, I mainly worked on learning latent variable models for NLP problems and ML problems.

We have some research intern positions available in Alibaba DAMO Academy. If you are interested in NLP and ML, please feel free to contact me: jiangyong.ml@@gmail.com.

Interests

Natural Language Processing
Machine Learning
Deep Learning

Education

PhD in Computer Science, 2019

ShanghaiTech University
PhD in Computer Science, 2019

University of Chinese Academy of Sciences

Experience

Research Intern

Tencent AI Lab

Jan 2018 – Oct 2018 Shenzhen

Research Intern

Tencent

Jul 2017 – Sep 2017 Shanghai

Visiting Scholar

UC Berkeley

Aug 2016 – Feb 2016 California

Selected Publications

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

September 2020 EMNLP 2020 Sequence Labeling

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network

Wenjuan Han, Liwen Zhang, Yong Jiang, Kewei Tu

September 2020 EMNLP 2020 Structured Prediction, Reinforcement Learning

Adversarial Attack and Defense of Structured Prediction Models

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu

September 2020 Findings of EMNLP 2020 Sequence Labeling

More Embeddings, Better Sequence Labelers?

Jun Li, Yifan Cao, Jiong Cai, Yong Jiang, Kewei Tu

July 2020 ACL 2020 Grammar Induction, Parsing

An Empirical Comparison of Unsupervised Constituency Parsing Methods

PDF Code Slides

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu

July 2020 ACL 2020 Sequence Labeling, Multi-lingual NLP

Structure-Level Knowledge Distillation For Multilingual Sequence Labeling

PDF Code Slides

Yong Jiang, Wenjuan Han, Kewei Tu

January 2019 EMNLP 2019 Grammar Induction, Parsing, Multi-lingual NLP

A Regularization-Based Framework for Bilingual Grammar Induction

PDF Poster

Yunzhe Yuan, Yong Jiang, Kewei Tu

January 2019 AAAI 2020 Parsing

Bidirectional Transition-Based Dependency Parsing

PDF Poster

Wenjuan Han, Yong Jiang, Kewei Tu

January 2019 ACL 2019 Grammar Induction, Parsing, Latent Variable Model

Enhancing Unsupervised Generative Dependency Parser with Contextual Information

PDF Poster

Wenjuan Han, Ge Wang, Yong Jiang, Kewei Tu

January 2019 EMNLP 2019 Grammar Induction, Parsing, Multi-lingual NLP

Multilingual Grammar Induction with Continuous Language Identification

PDF Code Slides

Jiong Cai, Yong Jiang, Kewei Tu

January 2017 EMNLP 2017 Grammar Induction, Parsing, Latent Variable Model

CRF Autoencoder for Unsupervised Dependency Parsing

PDF Code Poster

Yong Jiang, Wenjuan Han, Kewei Tu

January 2017 EMNLP 2017 Grammar Induction, Parsing

Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition

PDF Poster

Wenjuan Han, Yong Jiang, Kewei Tu

January 2017 EMNLP 2017 Grammar Induction, Parsing

Dependency Grammar Induction with Neural Lexicalization and Big Training Data

Shanbo Chu, Yong Jiang, Kewei Tu

January 2017 AAAI 2017 Latent Variable Model, Probabilistic Modeling

Latent Dependency Forest Models

Jun Mei, Yong Jiang, Kewei Tu

January 2017 Latent Variable Model, Probabilistic Modeling

Maximum A Posteriori Inference in Sum-Product Networks

Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, Dan Goldwasser

January 2017 EMNLP 2017 Sequence Labeling, Structured Prediction

Semi-supervised Structured Prediction with Neural CRF Autoencoder

Jiang Yong, Wenjuan Han, Kewei Tu

January 2016 EMNLP 2016 Grammar Induction, Parsing, Latent Variable Model

Unsupervised Neural Dependency Parsing