479 citations · 955 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…