World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
59
Citations
21081
World Ranking
170
National Ranking
20

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Li Dong is affiliated with Microsoft in the United States and specializes primarily in computer science with a focus on artificial intelligence. Their research encompasses several critical areas within the field, including natural language processing techniques, topic modeling, and multimodal machine learning applications.

Li Dong has contributed extensively to research, publishing a significant number of papers, particularly in artificial intelligence. Their recent work includes the following notable papers:

  • Unified language model pre-training for natural language understanding and generation (2024, arXiv [Cornell University])
  • MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers (2020, arXiv [Cornell University])
  • UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training (2020, arXiv [Cornell University])
  • Self-Attention Attribution: Interpreting Information Interactions Inside Transformer (2021, Proceedings of the AAAI Conference on Artificial Intelligence)
  • Knowledge Neurons in Pretrained Transformers (2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics [Volume 1: Long Papers])

Their frequent collaborators include Furu Wei, Shaohan Huang, Saksham Singhal, Song Xia, and Shuming Ma, with whom they have co-authored several papers. The collaborative nature of their work has contributed to the development of various models and techniques in machine learning and language understanding.

Common venues for Li Dong's publications are well-known and include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • World Wide Web

Li Dong's work systematically addresses a variety of subfields within computer science, with notable contributions to artificial intelligence, computer vision and pattern recognition, and information systems, as well as electrical and electronic engineering and control and systems engineering.

The main topics of Li Dong's research include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Speech Recognition and Synthesis
  • Explainable Artificial Intelligence (XAI)
  • Reinforcement Learning in Robotics
  • Adversarial Robustness in Machine Learning

Best Publications

  • Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

    Xiujun Li;Xi Yin;Chunyuan Li;Pengchuan Zhang

  • Long Short-Term Memory-Networks for Machine Reading

    Jianpeng Cheng;Li Dong;Mirella Lapata

  • Unified Language Model Pre-training for Natural Language Understanding and Generation

    Li Dong;Nan Yang;Wenhui Wang;Furu Wei

  • Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification

    Li Dong;Furu Wei;Chuanqi Tan;Duyu Tang

  • BEiT: BERT Pre-Training of Image Transformers

    Hangbo Bao;Li Dong;Furu Wei

  • Language to Logical Form with Neural Attention

    Li Dong;Mirella Lapata

  • MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

    Wenhui Wang;Furu Wei;Li Dong;Hangbo Bao

  • Question Answering over Freebase with Multi-Column Convolutional Neural Networks

    Li Dong;Furu Wei;Ming Zhou;Ke Xu

  • Coarse-to-Fine Decoding for Neural Semantic Parsing

    Li Dong;Mirella Lapata

  • MoodLens: an emoticon-based sentiment analysis system for chinese tweets

    Jichang Zhao;Li Dong;Junjie Wu;Ke Xu

  • Ranking with recursive neural networks and its application to multi-document summarization

    Ziqiang Cao;Furu Wei;Li Dong;Sujian Li

  • InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training

    Zewen Chi;Li Dong;Furu Wei;Nan Yang

  • Data-to-Text Generation with Content Selection and Planning

    Ratish Puduppully;Li Dong;Mirella Lapata

  • Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

    Ursula Challita;Li Dong;Walid Saad

  • Learning to Paraphrase for Question Answering

    Li Dong;Jonathan Mallinson;Siva Reddy;Mirella Lapata

  • Visualizing and Understanding the Effectiveness of BERT.

    Yaru Hao;Li Dong;Furu Wei;Ke Xu

  • Learning to Generate Product Reviews from Attributes

    Li Dong;Shaohan Huang;Furu Wei;Mirella Lapata

  • VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts

    Wenhui Wang;Hangbo Bao;Li Dong;Furu Wei

  • MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers

    Wenhui Wang;Hangbo Bao;Shaohan Huang;Li Dong

  • Pseudo-Masked Language Models for Unified Language Model Pre-Training

    Hangbo Bao;Li Dong;Furu Wei;Wenhui Wang

Frequent Co-Authors

Furu Wei
Furu Wei Microsoft (United States)
Ming Zhou
Ming Zhou Langboat Technology
Ke Xu
Ke Xu Beihang University
Mirella Lapata
Mirella Lapata University of Edinburgh
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Hsiao-Wuen Hon
Hsiao-Wuen Hon Microsoft Research Asia (China)
Ting Liu
Ting Liu Harbin Institute of Technology
Bing Qin
Bing Qin Harbin Institute of Technology
Wanxiang Che
Wanxiang Che Harbin Institute of Technology
Walid Saad
Walid Saad Virginia Tech

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