Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
concepts-to-text
License:
File size: 3,066 Bytes
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{
"default": {
"description": "CommonGen is a constrained text generation task, associated with a benchmark dataset,\nto explicitly test machines for the ability of generative commonsense reasoning. Given\na set of common concepts; the task is to generate a coherent sentence describing an\neveryday scenario using these concepts.\n\nCommonGen is challenging because it inherently requires 1) relational reasoning using\nbackground commonsense knowledge, and 2) compositional generalization ability to work\non unseen concept combinations. Our dataset, constructed through a combination of\ncrowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and\n50k sentences in total.\n",
"citation": "@inproceedings{lin-etal-2020-commongen,\n title = \"{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning\",\n author = \"Lin, Bill Yuchen and\n Zhou, Wangchunshu and\n Shen, Ming and\n Zhou, Pei and\n Bhagavatula, Chandra and\n Choi, Yejin and\n Ren, Xiang\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.findings-emnlp.165\",\n doi = \"10.18653/v1/2020.findings-emnlp.165\",\n pages = \"1823--1840\"\n}\n",
"homepage": "https://inklab.usc.edu/CommonGen/index.html",
"license": "",
"features": {
"concept_set_idx": {
"dtype": "int32",
"_type": "Value"
},
"concepts": {
"feature": {
"dtype": "string",
"_type": "Value"
},
"_type": "Sequence"
},
"target": {
"dtype": "string",
"_type": "Value"
}
},
"supervised_keys": {
"input": "concepts",
"output": "target"
},
"builder_name": "common_gen",
"dataset_name": "common_gen",
"config_name": "default",
"version": {
"version_str": "2020.5.30",
"major": 2020,
"minor": 5,
"patch": 30
},
"splits": {
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"name": "train",
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"num_examples": 67389,
"dataset_name": null
},
"validation": {
"name": "validation",
"num_bytes": 408740,
"num_examples": 4018,
"dataset_name": null
},
"test": {
"name": "test",
"num_bytes": 77518,
"num_examples": 1497,
"dataset_name": null
}
},
"download_size": 3434865,
"dataset_size": 7210424,
"size_in_bytes": 10645289
}
} |