no code implementations • ICLR 2019 • Tiago Ramalho, Tomas Kocisky, Frederic Besse, S. M. Ali Eslami, Gabor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.
1 code implementation • 4 Mar 2019 • Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Denis Teplyashin, Karl Moritz Hermann, Mateusz Malinowski, Matthew Koichi Grimes, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell
These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simultaneously with the perception and planning, is relatively unsupported.
1 code implementation • 1 Mar 2019 • Karl Moritz Hermann, Mateusz Malinowski, Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Raia Hadsell
Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision.
1 code implementation • 4 Jul 2018 • Tiago Ramalho, Tomáš Kočiský, Frederic Besse, S. M. Ali Eslami, Gábor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.
no code implementations • ICLR 2019 • Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas
We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure.
1 code implementation • ICLR 2019 • Gábor Melis, Charles Blundell, Tomáš Kočiský, Karl Moritz Hermann, Chris Dyer, Phil Blunsom
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the original dropout objective.
Ranked #24 on Language Modelling on Penn Treebank (Word Level)
1 code implementation • ICLR 2018 • Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark
The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks.
4 code implementations • NeurIPS 2018 • Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell
We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away.
no code implementations • ICLR 2018 • Felix Hill, Karl Moritz Hermann, Phil Blunsom, Stephen Clark
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds.
2 code implementations • TACL 2018 • Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette
Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document.
Ranked #9 on Question Answering on NarrativeQA (BLEU-1 metric)
no code implementations • ICLR 2018 • Felix Hill, Stephen Clark, Karl Moritz Hermann, Phil Blunsom
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds.
1 code implementation • 20 Jun 2017 • Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, Phil Blunsom
Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.
no code implementations • EMNLP 2016 • Tomáš Kočiský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing.
2 code implementations • ACL 2016 • Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.
Ranked #10 on Code Generation on Django
7 code implementations • 22 Sep 2015 • Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom
We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases.
Ranked #83 on Natural Language Inference on SNLI
11 code implementations • NeurIPS 2015 • Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
Teaching machines to read natural language documents remains an elusive challenge.
Ranked #13 on Question Answering on CNN / Daily Mail
4 code implementations • NeurIPS 2015 • Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
Recently, strong results have been demonstrated by Deep Recurrent Neural Networks on natural language transduction problems.
2 code implementations • 4 Dec 2014 • Lei Yu, Karl Moritz Hermann, Phil Blunsom, Stephen Pulman
Answer sentence selection is the task of identifying sentences that contain the answer to a given question.
Ranked #3 on Question Answering on QASent
no code implementations • 12 Nov 2014 • Karl Moritz Hermann
The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP.
no code implementations • ACL 2014 • Tomáš Kočiský, Karl Moritz Hermann, Phil Blunsom
We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data.
no code implementations • WS 2014 • Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann
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 to ontology-specific queries.
1 code implementation • ACL 2014 • 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.
Cross-Lingual Document Classification Document Classification +2
1 code implementation • 20 Dec 2013 • Karl Moritz Hermann, Phil Blunsom
Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP.
Cross-Lingual Document Classification Document Classification +3
no code implementations • 10 Jun 2013 • Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom
With the increasing empirical success of distributional models of compositional semantics, it is timely to consider the types of textual logic that such models are capable of capturing.