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e-SNLI: Natural Language Inference with Natural Language Explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz +1
In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from…
Stable Opponent Shaping in Differentiable Games
Alistair Letcher, Jakob Foerster, David Balduzzi +2
A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity t…
Interpretation of Natural Language Rules in Conversational Machine Reading
Marzieh Saeidi, Max Bartolo, Patrick Lewis +5
Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems…
Towards Neural Theorem Proving at Scale
Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel +1
Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their c…
Jack the Reader - A Machine Reading Framework
Dirk Weissenborn, Pasquale Minervini, Tim Dettmers +8
Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text…
DiCE: The Infinitely Differentiable Monte-Carlo Estimator
Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat +3
The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), eg, in reinforcement learning and meta-learnin…