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ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.
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Social IQA: Commonsense Reasoning about Social Interactions

It is established that Social IQa, the first large-scale benchmark for commonsense reasoning about social situations, is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap).
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WINOGRANDE: An Adversarial Winograd Schema Challenge at Scale

This work introduces 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, and establishes new state-of-the-art results on five related benchmarks.
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PIQA: Reasoning about Physical Commonsense in Natural Language

The task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA are introduced and analysis about the dimensions of knowledge that existing models lack are provided, which offers significant opportunities for future research.
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Abductive Commonsense Reasoning

This study introduces a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations, and conceptualizes two new tasks -- Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and Abduction NLG: a conditional generation task for explaining given observations in natural language.
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COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

It is proposed that manually constructed CSKGs will never achieve the coverage necessary to be applicable in all situations encountered by NLP agents, and a new evaluation framework for testing the utility of KGs based on how effectively implicit knowledge representations can be learned from them is proposed.
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Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

This paper introduces Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions, and proposes a new architecture that improves over the competitive baselines.
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Unsupervised Commonsense Question Answering with Self-Talk

An unsupervised framework based on self-talk as a novel alternative to multiple-choice commonsense tasks, inspired by inquiry-based discovery learning, which improves performance on several benchmarks and competes with models that obtain knowledge from external KBs.
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Adversarial Filters of Dataset Biases

This work presents extensive supporting evidence that AFLite is broadly applicable for reduction of measurable dataset biases, and that models trained on the filtered datasets yield better generalization to out-of-distribution tasks.
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UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark

Two new ways to evaluate commonsense models are proposed, emphasizing their generality on new tasks and building on diverse, recently introduced benchmarks, and a novel evaluation, the cost equivalent curve, is proposed, that sheds new insight on how the choice of source datasets, pretrained language models, and transfer learning methods impacts performance and data efficiency.
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