Generative Data Augmentation for Commonsense Reasoning

Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit on... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Question Answering CODAH G-DAUG-Combo + RoBERTa-Large Accuracy 84.0 # 1

Methods used in the Paper


METHOD TYPE
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