World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
8807
World Ranking
12395
National Ranking
5023

Overview

David Chiang is affiliated with the University of Notre Dame in the United States. Their research primarily spans the field of Computer Science, with a particular focus on Artificial Intelligence and Computational Theory and Mathematics. Additional subfields of interest include Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, and Management Science and Operations Research.

The scientist's work covers a range of main topics, prominently featuring Natural Language Processing Techniques and Topic Modeling. Other notable areas of research include semigroups and automata theory, Text Readability and Simplification, Ferroelectric and Negative Capacitance Devices, Speech Recognition and Synthesis, and Formal Methods in Verification.

David Chiang has contributed extensively to scholarly publications, with frequent appearances in several venues. The most common publication platform is arXiv (Cornell University), where they have 37 publications. Other key venues include Transactions of the Association for Computational Linguistics with 2 publications, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the ACM on Programming Languages, and Engineering Optimization.

Recent papers by David Chiang include the following:

  • Overcoming a Theoretical Limitation of Self-Attention (2022), published in the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Exact Recursive Probabilistic Programming (2023), published in the Proceedings of the ACM on Programming Languages
  • Tighter Bounds on the Expressivity of Transformer Encoders (2023), available on arXiv (Cornell University)
  • Named Tensor Notation (2021), available on arXiv (Cornell University)

David Chiang also appears as a co-author in works with several frequent collaborators, including Dana Angluin, Lena Strobl, Brian DuSell, Ryan Cotterell, and Andy Yang.

Best Publications

  • A Hierarchical Phrase-Based Model for Statistical Machine Translation

    David Chiang

  • Hierarchical Phrase-Based Translation

    David Chiang

  • DyNet: The Dynamic Neural Network Toolkit

    Graham Neubig;Chris Dyer;Yoav Goldberg;Austin Matthews

  • Better k-best Parsing

    Liang Huang;David Chiang

  • Word Sense Disambiguation Improves Statistical Machine Translation

    Yee Seng Chan;Hwee Tou Ng;David Chiang

  • Forest Rescoring: Faster Decoding with Integrated Language Models

    Liang Huang;David Chiang

  • Online Large-Margin Training of Syntactic and Structural Translation Features

    David Chiang;Yuval Marton;Philip Resnik

  • 11,001 New Features for Statistical Machine Translation

    David Chiang;Kevin Knight;Wei Wang

  • Decoding with Large-Scale Neural Language Models Improves Translation

    Ashish Vaswani;Yinggong Zhao;Victoria Fossum;David Chiang

  • Statistical parsing with an automatically-extracted tree adjoining grammar

    David Chiang

  • Tied Multitask Learning for Neural Speech Translation

    Antonios Anastasopoulos;David Chiang

  • Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation

    Toan Q. Nguyen;David Chiang

  • An attentional model for speech translation without transcription

    Long Duong;Antonios Anastasopoulos;David Chiang;Steven Bird;Steven Bird

  • Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder

    Huadong Chen;Shujian Huang;David Chiang;Jiajun Chen

  • Learning to Translate with Source and Target Syntax

    David Chiang

  • Two Statistical Parsing Models Applied to the Chinese Treebank

    Daniel M. Bikel;David Chiang

  • Correcting Length Bias in Neural Machine Translation

    Kenton Murray;David Chiang

  • Parsing arabic dialects

    David Chiang;Mona T. Diab;Nizar Habash;Owen Rambow

  • Recovering latent information in treebanks

    David Chiang;Daniel M. Bikel

  • Improving Lexical Choice in Neural Machine Translation.

    Toan Q. Nguyen;David Chiang

Frequent Co-Authors

Ashish Vaswani
Ashish Vaswani Google (United States)
Dekai Wu
Dekai Wu Hong Kong University of Science and Technology
Kevin Knight
Kevin Knight University of Southern California
Aravind K. Joshi
Aravind K. Joshi University of Pennsylvania
Steven Bird
Steven Bird Charles Darwin University
Liang Huang
Liang Huang Oregon State University
Walter J. Scheirer
Walter J. Scheirer University of Notre Dame
Philip Resnik
Philip Resnik University of Maryland, College Park
Owen Rambow
Owen Rambow Stony Brook University
Daniel Gildea
Daniel Gildea University of Rochester

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