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 30 Citations 8,189 89 World Ranking 8651 National Ranking 4038

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Natural language processing

His primary scientific interests are in Artificial intelligence, Natural language processing, Speech recognition, Machine translation and Parsing. A large part of his Artificial intelligence studies is devoted to Phrase. His Natural language processing research includes elements of Explicit knowledge, Relation, Grammar, Written language and Arabic.

David Chiang interconnects Example-based machine translation, Speech translation, Translation and Transfer-based machine translation in the investigation of issues within Speech recognition. His work carried out in the field of Machine translation brings together such families of science as Syntax and Rule-based machine translation. His Syntax research incorporates elements of Machine learning and Syntax.

His most cited work include:

  • A Hierarchical Phrase-Based Model for Statistical Machine Translation (1086 citations)
  • Hierarchical Phrase-Based Translation (1056 citations)
  • DyNet: The Dynamic Neural Network Toolkit (337 citations)

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

His primary areas of study are Artificial intelligence, Natural language processing, Machine translation, Translation and Rule-based machine translation. His studies in Artificial intelligence integrate themes in fields like Machine learning and Speech recognition. David Chiang combines subjects such as Endangered language and Syntax with his study of Natural language processing.

His work deals with themes such as Language model and Syntax, which intersect with Machine translation. His Translation study incorporates themes from Tree, Word, String and Spoken language. The Rule-based machine translation study combines topics in areas such as Theoretical computer science, Computational linguistics, Probabilistic logic, Grammar and Graph.

He most often published in these fields:

  • Artificial intelligence (57.35%)
  • Natural language processing (47.06%)
  • Machine translation (30.88%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (57.35%)
  • Transformer (5.88%)
  • Theoretical computer science (19.12%)

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

David Chiang spends much of his time researching Artificial intelligence, Transformer, Theoretical computer science, Artificial neural network and BLEU. His Artificial intelligence research includes themes of Graph generation and Natural language processing. The concepts of his Natural language processing study are interwoven with issues in Context and Concatenation.

As part of one scientific family, David Chiang deals mainly with the area of Theoretical computer science, narrowing it down to issues related to the Set, and often Representation, Extension, Recursion, Probabilistic logic and Grammar. His Artificial neural network research is multidisciplinary, incorporating elements of Multiset, Automaton and Computational science. His biological study spans a wide range of topics, including Speech recognition and Training set.

Between 2018 and 2021, his most popular works were:

  • Neural Machine Translation of Text from Non-Native Speakers (15 citations)
  • Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation (9 citations)
  • Learning Hyperedge Replacement Grammars for Graph Generation (8 citations)

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

  • Artificial intelligence
  • Programming language
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Machine translation, BLEU, Transformer and Artificial neural network. His research integrates issues of Theoretical computer science and Data structure in his study of Artificial intelligence. His research in Theoretical computer science intersects with topics in Graph generation, Graph and Rule-based machine translation.

His BLEU research incorporates themes from Speech recognition, Training set and Grammar. His study in Transformer is interdisciplinary in nature, drawing from both Regularization, Random search and Hyperparameter.

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

A Hierarchical Phrase-Based Model for Statistical Machine Translation

David Chiang.
meeting of the association for computational linguistics (2005)

1532 Citations

Hierarchical Phrase-Based Translation

David Chiang.
Computational Linguistics (2007)

1400 Citations

DyNet: The Dynamic Neural Network Toolkit

Graham Neubig;Chris Dyer;Yoav Goldberg;Austin Matthews.
arXiv: Machine Learning (2017)

511 Citations

Better k-best Parsing

Liang Huang;David Chiang.
international workshop/conference on parsing technologies (2005)

410 Citations

Word Sense Disambiguation Improves Statistical Machine Translation

Yee Seng Chan;Hwee Tou Ng;David Chiang.
meeting of the association for computational linguistics (2007)

362 Citations

Forest Rescoring: Faster Decoding with Integrated Language Models

Liang Huang;David Chiang.
meeting of the association for computational linguistics (2007)

348 Citations

Online Large-Margin Training of Syntactic and Structural Translation Features

David Chiang;Yuval Marton;Philip Resnik.
empirical methods in natural language processing (2008)

299 Citations

11,001 New Features for Statistical Machine Translation

David Chiang;Kevin Knight;Wei Wang.
north american chapter of the association for computational linguistics (2009)

285 Citations

Decoding with Large-Scale Neural Language Models Improves Translation

Ashish Vaswani;Yinggong Zhao;Victoria Fossum;David Chiang.
empirical methods in natural language processing (2013)

278 Citations

Statistical parsing with an automatically-extracted tree adjoining grammar

David Chiang.
meeting of the association for computational linguistics (2000)

222 Citations

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