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Rapid Adaptation with Conditionally Shifted Neurons
Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri +1
We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionall…
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
Francis Dutil, Caglar Gulcehre, Adam Trischler +1
We investigate the integration of a planning mechanism into sequence-to-sequence models using attention. We develop a model which can plan ahead in the future when it computes its…
Variational Bi-LSTMs
Samira Shabanian, Devansh Arpit, Adam Trischler +1
Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forw…
FigureQA: An Annotated Figure Dataset for Visual Reasoning
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson +3
We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from…
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selectio…
Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
Caglar Gulcehre, Francis Dutil, Adam Trischler +1
We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation. We develop a model t…
A Joint Model for Question Answering and Question Generation
Tong Wang, Xingdi Yuan, Adam Trischler
We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework tha…
Neural Models for Key Phrase Detection and Question Generation
Sandeep Subramanian, Tong Wang, Xingdi Yuan +3
We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human…
Machine Comprehension by Text-to-Text Neural Question Generation
Xingdi Yuan, Tong Wang, Caglar Gulcehre +5
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervis…