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Computer Science

D-Index
78
Citations
43532
World Ranking
1164
National Ranking
617

Overview

Chris Dyer is affiliated with Google in the United States and actively contributes to the field of computer science with a focus on artificial intelligence and related subfields. Their research spans several key areas including natural language processing techniques, topic modeling, and machine learning and data classification.

The primary domains of their work include:

  • Natural Language Processing Techniques
  • Topic Modeling
  • Text Readability and Simplification
  • Handwritten Text Recognition Techniques
  • Expert Finding and Q&A Systems
  • Multimodal Machine Learning Applications
  • Machine Learning and Data Classification

The major subfields of study covered by Chris Dyer are:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Cognitive Neuroscience
  • Information Systems

Dyer has authored or contributed to 52 publications in computer science, with significant output in artificial intelligence. Their frequent publication venues include:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • Neuropsychologia
  • Computational Linguistics
  • Journal of Medical Ethics

Recent notable papers include:

  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems (2025, arXiv)
  • Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021, arXiv)
  • Localizing syntactic predictions using recurrent neural network grammars (2020, Neuropsychologia)
  • Machine Learning for Ancient Languages: A Survey (2023, Computational Linguistics)
  • End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering (2021, arXiv)

Throughout their career, Dyer has frequently collaborated with several researchers, including:

  • Adhiguna Kuncoro
  • Laurent Sartran
  • Phil Blunsom
  • Dani Yogatama
  • Kartik Goyal

Best Publications

  • Moses: Open Source Toolkit for Statistical Machine Translation

    Philipp Koehn;Hieu Hoang;Alexandra Birch;Chris Callison-Burch

  • Hierarchical Attention Networks for Document Classification

    Zichao Yang;Diyi Yang;Chris Dyer;Xiaodong He

  • Neural Architectures for Named Entity Recognition

    Guillaume Lample;Miguel Ballesteros;Sandeep Subramanian;Kazuya Kawakami

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Retrofitting Word Vectors to Semantic Lexicons

    Manaal Faruqui;Jesse Dodge;Sujay Kumar Jauhar;Chris Dyer

  • Transition-Based Dependency Parsing with Stack Long Short-Term Memory

    Chris Dyer;Miguel Ballesteros;Wang Ling;Austin Matthews

  • A Simple, Fast, and Effective Reparameterization of IBM Model 2

    Chris Dyer;Victor Chahuneau;Noah A. Smith

  • Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters

    Olutobi Owoputi;Brendan O'Connor;Chris Dyer;Kevin Gimpel

  • Data-Intensive Text Processing with MapReduce

    Jimmy Lin;Chris Dyer

  • Improving Vector Space Word Representations Using Multilingual Correlation

    Manaal Faruqui;Chris Dyer

  • Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

    Wang Ling;Chris Dyer;Alan W Black;Isabel Trancoso

  • Recurrent Neural Network Grammars

    Chris Dyer;Adhiguna Kuncoro;Miguel Ballesteros;Noah A. Smith

  • Learning Deep Generative Models of Graphs

    Yujia Li;Oriol Vinyals;Chris Dyer;Razvan Pascanu

  • The NarrativeQA Reading Comprehension Challenge

    Tomáš Kočiský;Jonathan Schwarz;Phil Blunsom;Chris Dyer

  • DyNet: The Dynamic Neural Network Toolkit

    Graham Neubig;Chris Dyer;Yoav Goldberg;Austin Matthews

  • Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability

    Jonathan H. Clark;Chris Dyer;Alon Lavie;Noah A. Smith

  • Two/Too Simple Adaptations of Word2Vec for Syntax Problems

    Wang Ling;Chris Dyer;Alan W. Black;Isabel Trancoso

  • On the State of the Art of Evaluation in Neural Language Models

    Gábor Melis;Chris Dyer;Phil Blunsom

  • Massively Multilingual Word Embeddings

    Waleed Ammar;George Mulcaire;Yulia Tsvetkov;Guillaume Lample

  • Gaussian LDA for Topic Models with Word Embeddings

    Rajarshi Das;Manzil Zaheer;Chris Dyer

  • Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

    Wang Ling;Tiago Luís;Luís Marujo;Ramón Fernandez Astudillo

  • Open Source Toolkit for Statistical Machine Translation: Factored Translation Models and Lattice Decoding

    Philipp Koehn;Marcello Federico;Wade Shen;Nicola Bertoldi

Frequent Co-Authors

Noah A. Smith
Noah A. Smith University of Washington
Phil Blunsom
Phil Blunsom University of Oxford
Yulia Tsvetkov
Yulia Tsvetkov University of Washington
Dani Yogatama
Dani Yogatama University of Southern California
Graham Neubig
Graham Neubig Carnegie Mellon University
Alan W. Black
Alan W. Black Carnegie Mellon University
Alon Lavie
Alon Lavie Carnegie Mellon University
Lori Levin
Lori Levin Carnegie Mellon University
Isabel Trancoso
Isabel Trancoso Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa

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