Timezone: »
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 human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model’s decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust
Author Information
Oana-Maria Camburu (University of Oxford)
Tim Rocktäschel (University of Oxford)
Tim Rocktäschel is a Research Scientist at Facebook AI Research (FAIR) London and a Lecturer in the Department of Computer Science at University College London (UCL). At UCL, he is a member of the UCL Centre for Artificial Intelligence and the UCL Natural Language Processing group. Prior to that, he was a Postdoctoral Researcher in the Whiteson Research Lab, a Stipendiary Lecturer in Computer Science at Hertford College, and a Junior Research Fellow in Computer Science at Jesus College, at the University of Oxford. Tim obtained his Ph.D. in the Machine Reading group at University College London under the supervision of Sebastian Riedel. He received a Google Ph.D. Fellowship in Natural Language Processing in 2017 and a Microsoft Research Ph.D. Scholarship in 2013. In Summer 2015, he worked as a Research Intern at Google DeepMind. In 2012, he obtained his Diploma (equivalent to M.Sc) in Computer Science from the Humboldt-Universität zu Berlin. Between 2010 and 2012, he worked as Student Assistant and in 2013 as Research Assistant in the Knowledge Management in Bioinformatics group at Humboldt-Universität zu Berlin. Tim's research focuses on sample-efficient and interpretable machine learning models that learn from world, domain, and commonsense knowledge in symbolic and textual form. His work is at the intersection of deep learning, reinforcement learning, natural language processing, program synthesis, and formal logic.
Thomas Lukasiewicz (University of Oxford)
Phil Blunsom (Oxford University)
More from the Same Authors
-
2020 Poster: The NetHack Learning Environment »
Heinrich Küttler · Nantas Nardelli · Alexander Miller · Roberta Raileanu · Marco Selvatici · Edward Grefenstette · Tim Rocktäschel -
2020 Poster: Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation »
Bowen Li · Xiaojuan Qi · Philip Torr · Thomas Lukasiewicz -
2020 Poster: Coherent Hierarchical Multi-Label Classification Networks »
Eleonora Giunchiglia · Thomas Lukasiewicz -
2020 Poster: BoxE: A Box Embedding Model for Knowledge Base Completion »
Ralph Abboud · Ismail Ceylan · Thomas Lukasiewicz · Tommaso Salvatori -
2020 Spotlight: BoxE: A Box Embedding Model for Knowledge Base Completion »
Ralph Abboud · Ismail Ceylan · Thomas Lukasiewicz · Tommaso Salvatori -
2020 Poster: Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks »
Yuhang Song · Thomas Lukasiewicz · Zhenghua Xu · Rafal Bogacz -
2019 Poster: Controllable Text-to-Image Generation »
Bowen Li · Xiaojuan Qi · Thomas Lukasiewicz · Philip Torr -
2017 Workshop: 6th Workshop on Automated Knowledge Base Construction (AKBC) »
Jay Pujara · Dor Arad · Bhavana Dalvi Mishra · Tim Rocktäschel -
2017 Poster: End-to-End Differentiable Proving »
Tim Rocktäschel · Sebastian Riedel -
2017 Oral: End-to-end Differentiable Proving »
Tim Rocktäschel · Sebastian Riedel -
2016 Workshop: Neural Abstract Machines & Program Induction »
Matko Bošnjak · Nando de Freitas · Tejas Kulkarni · Arvind Neelakantan · Scott E Reed · Sebastian Riedel · Tim Rocktäschel -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz