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Dataset: common_gen 🏷
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#### Description

CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total.

#### Citation

@article{lin2019comgen,
author = {Bill Yuchen Lin and Ming Shen and Wangchunshu Zhou and Pei Zhou and Chandra Bhagavatula and Yejin Choi and Xiang Ren},
title = {CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning},
journal = {CoRR},
volume = {abs/1911.03705},
year = {2019}
}

#### Models trained or fine-tuned on common_gen

None yet. Start fine-tuning now =)