Search Results for author:
Found 32 papers, 15 papers with code
Learning to Transduce with Unbounded Memory
Recently, strong results have been demonstrated by Deep Recurrent Neural Networks on natural language transduction problems.
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive challenge.
Ranked #13 on
Question Answering
on CNN / Daily Mail
Learning to Navigate in Cities Without a Map
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.
Deep Learning for Answer Sentence Selection
Answer sentence selection is the task of identifying sentences that contain the answer to a given question.
Ranked #3 on
Question Answering
on QASent
The NarrativeQA Reading Comprehension Challenge
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)
Learning To Follow Directions in Street View
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.
The StreetLearn Environment and Dataset
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.
Latent Predictor Networks for Code Generation
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.
Ranked #10 on
Code Generation
on Django
Reasoning about Entailment with Neural Attention
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
Pushing the bounds of dropout
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)
Multilingual Models for Compositional Distributed Semantics
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
Multilingual Distributed Representations without Word Alignment
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
Encoding Spatial Relations from Natural Language
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.
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
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.
Grounded Language Learning in a Simulated 3D World
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.
Hyperbolic Attention Networks
We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure.
Understanding Early Word Learning in Situated Artificial Agents
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.
Semantic Parsing with Semi-Supervised Sequential Autoencoders
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing.
Distributed Representations for Compositional Semantics
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.
Learning Bilingual Word Representations by Marginalizing Alignments
We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data.
A Deep Architecture for Semantic Parsing
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.
"Not not bad" is not "bad": A distributional account of negation
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.
Learning to encode spatial relations from natural language
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.
Understanding Grounded Language Learning Agents
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.
