2.7k citations · 6.8k across the 64 of their papers we have counts for
11 papers · 1 filter
Bayesian Optimisation for Machine Translation
Yishu Miao, Ziyu Wang, Phil Blunsom
This paper presents novel Bayesian optimisation algorithms for minimum error rate training of statistical machine translation systems. We explore two classes of algorithms for effi…
Deep Multi-Instance Transfer Learning
Dimitrios Kotzias, Misha Denil, Phil Blunsom +1
We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences…
Deep Learning for Answer Sentence Selection
Lei Yu, Karl Moritz Hermann, Phil Blunsom +1
Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger c…
Pragmatic Neural Language Modelling in Machine Translation
Paul Baltescu, Phil Blunsom
This paper presents an in-depth investigation on integrating neural language models in translation systems. Scaling neural language models is a difficult task, but crucial for real…
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
Misha Denil, Alban Demiraj, Nal Kalchbrenner +2
Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retri…
Compositional Morphology for Word Representations and Language Modelling
Jan A. Botha, Phil Blunsom
This paper presents a scalable method for integrating compositional morphological representations into a vector-based probabilistic language model. Our approach is evaluated in the…
Learning Bilingual Word Representations by Marginalizing Alignments
Tomáš Kočiský, Karl Moritz Hermann, Phil Blunsom
We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data. By marginalizing over word alignments the model captures…
A Deep Architecture for Semantic Parsing
Edward Grefenstette, Phil Blunsom, Nando de Freitas +1
Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses…
Multilingual Models for Compositional Distributed Semantics
Karl Moritz Hermann, Phil Blunsom
We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage…
A Convolutional Neural Network for Modelling Sentences
Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN)…
Modelling the Lexicon in Unsupervised Part of Speech Induction
Greg Dubbin, Phil Blunsom
Automatically inducing the syntactic part-of-speech categories for words in text is a fundamental task in Computational Linguistics. While the performance of unsupervised tagging m…