Ronan Le Bras

Allen Institute for Artificial Intelligence
0000-0001-6784-1312
http://rlebras.github.io/
Publications73
h-index17
Citations1,230
Highly Influential Citations189
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  • Publications
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ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
TLDR
We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge about events described in natural language and associated with typed if-then relations. Expand
Social IQA: Commonsense Reasoning about Social Interactions
TLDR
We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Expand
WINOGRANDE: An Adversarial Winograd Schema Challenge at Scale
TLDR
We introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. Expand
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
TLDR
We introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. Expand
Abductive Commonsense Reasoning
TLDR
In this paper, we present the first study that investigates the viability of language-based abductive reasoning. Expand
PIQA: Reasoning about Physical Commonsense in Natural Language
TLDR
We introduce the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA to evaluate language representations on their knowledge of physical Commonsense. Expand
Adversarial Filters of Dataset Biases
TLDR
We investigate one recently proposed approach, AFLite, which adversarially filters such dataset biases, as a means to mitigate the prevalent overestimation of machine performance. Expand
Unsupervised Commonsense Question Answering with Self-Talk
TLDR
We propose an unsupervised framework based on \emph{self-talk} as a novel alternative to multiple-choice commonsense tasks, and compete with models that obtain knowledge from external KBs. Expand
On the Erdős Discrepancy Problem
TLDR
We prove that any completely multiplicative sequence of size 127,646 or more has discrepancy at least 4, proving the Erdős discrepancy conjecture for discrepancy up to 3. Expand
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery
TLDR
We introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints. Expand
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