World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
24134
World Ranking
3335
National Ranking
200

Overview

Phil Blunsom is affiliated with the University of Oxford in the United Kingdom and has contributed extensively to the field of computer science, with a focus on artificial intelligence and related subfields. Their research emphasizes topics such as topic modeling, natural language processing techniques, text readability and simplification, multimodal machine learning applications, speech and dialogue systems, speech recognition and synthesis, and explainable artificial intelligence (XAI).

The scientist has coauthored numerous papers with frequent collaborators including Satwik Bhattamishra, Adhiguna Kuncoro, Dani Yogatama, Chris Dyer, and Cyprien de Masson d'Autume.

Phil Blunsom's publication record includes contributions to prominent venues such as:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • IEEE Transactions on Neural Networks and Learning Systems

Selected recent papers include:

  • Mind the Gap: Assessing Temporal Generalization in Neural Language Models, 2021, arXiv (Cornell University)
  • Learning With Stochastic Guidance for Robot Navigation, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • Pretraining the Noisy Channel Model for Task-Oriented Dialogue, 2021, Transactions of the Association for Computational Linguistics
  • Relational Memory-Augmented Language Models, 2022, Transactions of the Association for Computational Linguistics

The focus of research integrates the analysis and development of machine learning models capable of enhanced understanding and processing of language, alongside multimodal applications and systems supporting speech and dialogue. This body of work reflects a sustained involvement in advancing computational approaches within artificial intelligence.

The scientist's work spans multiple subfields including artificial intelligence, computer vision and pattern recognition, signal processing, and electrical and electronic engineering. The main field of study is computer science.

Best Publications

  • A Convolutional Neural Network for Modelling Sentences

    Nal Kalchbrenner;Edward Grefenstette;Phil Blunsom

  • Teaching machines to read and comprehend

    Karl Moritz Hermann;Tomáš Kočiský;Edward Grefenstette;Lasse Espeholt

  • Hybrid computing using a neural network with dynamic external memory

    Alex Graves;Greg Wayne;Malcolm Reynolds;Tim Harley

  • Recurrent Continuous Translation Models

    Nal Kalchbrenner;Phil Blunsom

  • Reasoning about Entailment with Neural Attention

    Tim Rocktäschel;Edward Grefenstette;Karl Moritz Hermann;Tomáš Ko iský;Tomáš Ko iský

  • Reasoning about Entailment with Neural Attention

    Tim Rocktäschel;Edward Grefenstette;Karl Moritz Hermann;Tomáš Kočiský

  • The NarrativeQA Reading Comprehension Challenge

    Tomáš Kočiský;Jonathan Schwarz;Phil Blunsom;Chris Dyer

  • Neural variational inference for text processing

    Yishu Miao;Lei Yu;Phil Blunsom

  • e-SNLI: Natural Language Inference with Natural Language Explanations

    Oana-Maria Camburu;Tim Rocktäschel;Thomas Lukasiewicz;Phil Blunsom

  • Deep learning for answer sentence selection

    Lei Yu;Karl Moritz Hermann;Phil Blunsom;Stephen Pulman

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

    Gábor Melis;Chris Dyer;Phil Blunsom

  • Latent Predictor Networks for Code Generation

    Wang Ling;Phil Blunsom;Edward Grefenstette;Karl Moritz Hermann

  • Multilingual Models for Compositional Distributed Semantics

    Karl Moritz Hermann;Phil Blunsom

  • cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models

    Chris Dyer;Adam Lopez;Juri Ganitkevitch;Jonathan Weese

  • Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

    Wang Ling;Dani Yogatama;Chris Dyer;Phil Blunsom

  • Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

    Zhuoling Xiao;Hongkai Wen;Andrew Markham;Niki Trigoni

  • Learning to transduce with unbounded memory

    Edward Grefenstette;Karl Moritz Hermann;Mustafa Suleyman;Phil Blunsom

  • Compositional Morphology for Word Representations and Language Modelling

    Jan Botha;Phil Blunsom

  • Grounded Language Learning in a Simulated 3D World

    Karl Moritz Hermann;Felix Hill;Simon Green;Fumin Wang

  • Learning and Evaluating General Linguistic Intelligence.

    Dani Yogatama;Cyprien de Masson d'Autume;Jerome T. Connor;Tomás Kociský

  • Recurrent Convolutional Neural Networks for Discourse Compositionality

    Nal Kalchbrenner;Phil Blunsom

Frequent Co-Authors

Chris Dyer
Chris Dyer Google (United States)
Edward Grefenstette
Edward Grefenstette University College London
Trevor Cohn
Trevor Cohn University of Melbourne
Dani Yogatama
Dani Yogatama University of Southern California
Stephen Clark
Stephen Clark Cambridge Quantum Computing
Thomas Lukasiewicz
Thomas Lukasiewicz University of Oxford
Miles Osborne
Miles Osborne Bloomberg LP
Niki Trigoni
Niki Trigoni University of Oxford
Sharon Goldwater
Sharon Goldwater University of Edinburgh
Felix Hill
Felix Hill Google (United States)

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