D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 3,882 110 World Ranking 7345 National Ranking 3486

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Machine learning

Nan Duan mainly investigates Artificial intelligence, Question answering, Natural language processing, Information retrieval and Recurrent neural network. Many of his studies on Artificial intelligence apply to Information flow as well. Nan Duan has included themes like DUAL, Task analysis, Knowledge extraction, Scheme and Visualization in his Question answering study.

His Natural language processing research incorporates themes from Commonsense reasoning, Set and Transformer. The various areas that Nan Duan examines in his Information retrieval study include Relation, Natural language and Chatbot. His Recurrent neural network research is multidisciplinary, relying on both Question generation, Probabilistic logic and Leverage.

His most cited work include:

  • Question Generation for Question Answering (135 citations)
  • Question Answering and Question Generation as Dual Tasks (106 citations)
  • Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training (98 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Artificial intelligence, Natural language processing, Question answering, Information retrieval and Natural language. Artificial intelligence is frequently linked to Machine learning in his study. His work on Parsing as part of general Natural language processing study is frequently connected to Encoder, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

His Parsing study combines topics from a wide range of disciplines, such as Artificial neural network, Multi-task learning, SQL and Logical form. His Question answering study combines topics in areas such as Question generation, Leverage and Knowledge base. He has researched Benchmark in several fields, including Natural language understanding, Set and Task.

He most often published in these fields:

  • Artificial intelligence (70.87%)
  • Natural language processing (54.33%)
  • Question answering (29.13%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (70.87%)
  • Natural language processing (54.33%)
  • Benchmark (10.24%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Natural language processing, Benchmark, Machine learning and Transformer. His study in the field of Natural language and Question answering also crosses realms of Natural and Encoder. His work carried out in the field of Question answering brings together such families of science as Dependency grammar, Information flow, Graph based and Knowledge base.

His Natural language processing study integrates concerns from other disciplines, such as Machine reading, Comprehension and Generative grammar. In the subject of general Machine learning, his work in Overfitting is often linked to Base and Scale, thereby combining diverse domains of study. His Transformer research includes themes of Language model, Rewriting, Layer and Continuous embedding.

Between 2019 and 2021, his most popular works were:

  • K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (76 citations)
  • XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation (64 citations)
  • Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training. (58 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Programming language
  • Machine learning

Nan Duan mostly deals with Artificial intelligence, Natural language processing, Machine learning, Transformer and Benchmark. Many of his research projects under Artificial intelligence are closely connected to Adapter with Adapter, tying the diverse disciplines of science together. His studies deal with areas such as Inference, Graph based, Commonsense knowledge, Knowledge base and Information flow as well as Question answering.

His work on Natural language understanding as part of general Natural language processing research is frequently linked to Paragraph, bridging the gap between disciplines. His Machine learning research integrates issues from n-gram and Automatic summarization. The concepts of his Transformer study are interwoven with issues in Language model, Documentation and Natural language.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training.

Gen Li;Nan Duan;Yuejian Fang;Ming Gong.
national conference on artificial intelligence (2020)

255 Citations

Question Generation for Question Answering

Nan Duan;Duyu Tang;Peng Chen;Ming Zhou.
empirical methods in natural language processing (2017)

169 Citations

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters.

Ruize Wang;Duyu Tang;Nan Duan;zhongyu wei.
arXiv: Computation and Language (2020)

159 Citations

Knowledge-Based Question Answering as Machine Translation

Junwei Bao;Nan Duan;Ming Zhou;Tiejun Zhao.
meeting of the association for computational linguistics (2014)

159 Citations

Question Answering and Question Generation as Dual Tasks

Duyu Tang;Nan Duan;Tao Qin;Zhao Yan.
arXiv: Computation and Language (2017)

158 Citations

Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training

Gen Li;Nan Duan;Yuejian Fang;Ming Gong.
arXiv: Computer Vision and Pattern Recognition (2019)

143 Citations

Constraint-Based Question Answering with Knowledge Graph

Junwei Bao;Nan Duan;Zhao Yan;Ming Zhou.
international conference on computational linguistics (2016)

142 Citations

XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation

Yaobo Liang;Nan Duan;Yeyun Gong;Ning Wu.
empirical methods in natural language processing (2020)

128 Citations

Building Task-Oriented Dialogue Systems for Online Shopping.

Zhao Yan;Nan Duan;Peng Chen;Ming Zhou.
national conference on artificial intelligence (2017)

128 Citations

Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks.

Haoyang Huang;Yaobo Liang;Nan Duan;Ming Gong.
arXiv: Computation and Language (2019)

123 Citations

Best Scientists Citing Nan Duan

Xiang Ren

Xiang Ren

University of Southern California

Publications: 27

Zhe Gan

Zhe Gan

Microsoft (United States)

Publications: 26

Jingjing Liu

Jingjing Liu

Tsinghua University

Publications: 22

Graham Neubig

Graham Neubig

Carnegie Mellon University

Publications: 22

Pascale Fung

Pascale Fung

Hong Kong University of Science and Technology

Publications: 22

Furu Wei

Furu Wei

Microsoft (United States)

Publications: 20

Iryna Gurevych

Iryna Gurevych

University of Paderborn

Publications: 20

Gerhard Weikum

Gerhard Weikum

Max Planck Institute for Informatics

Publications: 19

Ming Zhou

Ming Zhou

Sinovation Ventures

Publications: 19

Heng Ji

Heng Ji

University of Illinois at Urbana-Champaign

Publications: 17

Yu Cheng

Yu Cheng

Microsoft (United States)

Publications: 17

Qun Liu

Qun Liu

Huawei Technologies (China)

Publications: 17

Daxin Jiang

Daxin Jiang

Microsoft (United States)

Publications: 16

Jianfeng Gao

Jianfeng Gao

Microsoft (United States)

Publications: 16

Mohit Bansal

Mohit Bansal

University of North Carolina at Chapel Hill

Publications: 15

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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