Blunsom, Phil

A Convolutional Neural Network for Modelling Sentences

Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 3,601

The NarrativeQA Reading Comprehension Challenge

Kočiský, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., & Grefenstette, E. (2018) · Transactions of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 596

e-SNLI: Natural language inference with natural language explanations

Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., & Blunsom, P. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 367

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

Ling, W., Yogatama, D., Dyer, C., & Blunsom, P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 325

Latent Predictor Networks for Code Generation

Ling, W., Blunsom, P., Grefenstette, E., Hermann, K. M., Kočiský, T., Wang, F., & Senior, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Software Engineering ResearchTopic Modeling

Cited by 307

Mind the Gap: Assessing Temporal Generalization in Neural Language Models

Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liska, A., Terzi, T., Gimenez, M., d'Autume, C. D. M., Kocisky, T., Ruder, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 90

Relational memory-augmented language models

Liu, Q., Yogatama, D., & Blunsom, P. (2022) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 23

StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models

Liška, A., Kočiský, T., Gribovskaya, E., Terzi, T., Sezener, E., Agrawal, D., d'Autume, C. D. M., Scholtes, T., Zaheer, M., Young, S., et al. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 19

Human Feedback is not Gold Standard

Hosking, T., Blunsom, P., & Bartolo, M. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions

Bhattamishra, S., Patel, A., Blunsom, P., & Kanade, V. (2023) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 3

Separations in the Representational Capabilities of Transformers and Recurrent Architectures

Bhattamishra, S., Hahn, M., Blunsom, P., & Kanade, V. (2024) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 0