Datasets:
GEM
/

Languages:
English
Multilinguality:
unknown
Size Categories:
unknown
Language Creators:
unknown
Annotations Creators:
none
Source Datasets:
original
Tags:
data-to-text
License:
File size: 7,746 Bytes
3fb328f
1
{"default": {"description": "The Conversational Weather dataset is designed for generation of responses to weather queries based on a structured input data. The input allows specifying data attributes such as dates, times, locations, weather conditions, and errors, and also offers control over structure of response through discourse relations such as join, contrast, and justification.\n", "citation": "@inproceedings{balakrishnan-etal-2019-constrained,\n  title = \"Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue\",\n  author = \"Balakrishnan, Anusha  and\n    Rao, Jinfeng  and\n    Upasani, Kartikeya  and\n    White, Michael  and\n    Subba, Rajen\",\n  booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n  month = jul,\n  year = \"2019\",\n  address = \"Florence, Italy\",\n  publisher = \"Association for Computational Linguistics\",\n  url = \"https://www.aclweb.org/anthology/P19-1080\",\n  doi = \"10.18653/v1/P19-1080\",\n  pages = \"831--844\"\n}\n", "homepage": "https://github.com/facebookresearch/TreeNLG", "license": "CC-BY-NC-4.0", "features": {"gem_id": {"dtype": "string", "id": null, "_type": "Value"}, "data_id": {"dtype": "string", "id": null, "_type": "Value"}, "user_query": {"dtype": "string", "id": null, "_type": "Value"}, "tree_str_mr": {"dtype": "string", "id": null, "_type": "Value"}, "response": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"dtype": "string", "id": null, "_type": "Value"}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "conversational_weather", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 36514596, "num_examples": 25390, "dataset_name": "conversational_weather"}, "test": {"name": "test", "num_bytes": 4544810, "num_examples": 3121, "dataset_name": "conversational_weather"}, "validation": {"name": "validation", "num_bytes": 4399453, "num_examples": 3078, "dataset_name": "conversational_weather"}}, "download_checksums": {"https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/train.tsv": {"num_bytes": 18982974, "checksum": "5d3d27566eb2ac6c8d5295a78072209a78670c8d8ab8e20443d678e9bd2501db"}, "https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/val.tsv": {"num_bytes": 2292601, "checksum": "7e50b746de247c5ac9bae1f53381e8230856edc43ec9460601fc2577fbe77286"}, "https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/test.tsv": {"num_bytes": 2365273, "checksum": "f2a36007698e510e3308d7fa4a23c2b492321047d849ed5e04409cb773b3b1ca"}}, "download_size": 23640848, "post_processing_size": null, "dataset_size": 45458859, "size_in_bytes": 69099707}, "challenge": {"description": "The Conversational Weather dataset is designed for generation of responses to weather queries based on a structured input data. The input allows specifying data attributes such as dates, times, locations, weather conditions, and errors, and also offers control over structure of response through discourse relations such as join, contrast, and justification.\n", "citation": "@inproceedings{balakrishnan-etal-2019-constrained,\n  title = \"Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue\",\n  author = \"Balakrishnan, Anusha  and\n    Rao, Jinfeng  and\n    Upasani, Kartikeya  and\n    White, Michael  and\n    Subba, Rajen\",\n  booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n  month = jul,\n  year = \"2019\",\n  address = \"Florence, Italy\",\n  publisher = \"Association for Computational Linguistics\",\n  url = \"https://www.aclweb.org/anthology/P19-1080\",\n  doi = \"10.18653/v1/P19-1080\",\n  pages = \"831--844\"\n}\n", "homepage": "https://github.com/facebookresearch/TreeNLG", "license": "CC-BY-NC-4.0", "features": {"gem_id": {"dtype": "string", "id": null, "_type": "Value"}, "data_id": {"dtype": "string", "id": null, "_type": "Value"}, "user_query": {"dtype": "string", "id": null, "_type": "Value"}, "tree_str_mr": {"dtype": "string", "id": null, "_type": "Value"}, "response": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"dtype": "string", "id": null, "_type": "Value"}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "conversational_weather", "config_name": "challenge", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"disc_test": {"name": "disc_test", "num_bytes": 460468, "num_examples": 263, "dataset_name": "conversational_weather"}, "disc_test_freq_0": {"name": "disc_test_freq_0", "num_bytes": 2071610, "num_examples": 1685, "dataset_name": "conversational_weather"}, "disc_test_freq_1": {"name": "disc_test_freq_1", "num_bytes": 2025862, "num_examples": 1208, "dataset_name": "conversational_weather"}, "disc_test_freq_2": {"name": "disc_test_freq_2", "num_bytes": 464858, "num_examples": 220, "dataset_name": "conversational_weather"}, "disc_test_freq_3": {"name": "disc_test_freq_3", "num_bytes": 24806, "num_examples": 8, "dataset_name": "conversational_weather"}, "dial_test_freq_1": {"name": "dial_test_freq_1", "num_bytes": 619371, "num_examples": 608, "dataset_name": "conversational_weather"}, "dial_test_freq_2": {"name": "dial_test_freq_2", "num_bytes": 1435082, "num_examples": 1138, "dataset_name": "conversational_weather"}, "dial_test_freq_3": {"name": "dial_test_freq_3", "num_bytes": 1517612, "num_examples": 923, "dataset_name": "conversational_weather"}, "dial_test_freq_4": {"name": "dial_test_freq_4", "num_bytes": 844282, "num_examples": 396, "dataset_name": "conversational_weather"}, "dial_test_freq_5": {"name": "dial_test_freq_5", "num_bytes": 169906, "num_examples": 56, "dataset_name": "conversational_weather"}}, "download_checksums": {"./data/challenge_sets/disc_test.tsv": {"num_bytes": 240759, "checksum": "142608abfb4cae43b760cd7cd5788ce9c599f8c43671ea011a0d16c1dca40b58"}, "./data/challenge_sets/dial_test_freq_1.tsv": {"num_bytes": 296303, "checksum": "3057b7c34ce66ebaadcb22ff5834158b80af5a1a5f3ce92c5b2d237c31d84feb"}, "./data/challenge_sets/dial_test_freq_2.tsv": {"num_bytes": 729082, "checksum": "af3b7471015b87af6fb118ff5d97b12c07070cdd6256498105a0f7be060fc4e5"}, "./data/challenge_sets/dial_test_freq_3.tsv": {"num_bytes": 798326, "checksum": "f2999eca340417ecbecead1719df3a9ae8fe0d8afc6624212fa2dc465d18357a"}, "./data/challenge_sets/dial_test_freq_4.tsv": {"num_bytes": 450130, "checksum": "678041916baa5f9cd5d415638689b85c0e8e3fdc9a9fe66273a1342cfd4ba0d4"}, "./data/challenge_sets/dial_test_freq_5.tsv": {"num_bytes": 91432, "checksum": "10bf51037283c8322d3aaea64e6b1ba5e3ae6c45ed621e327eabdf791ce6c1b2"}, "./data/challenge_sets/disc_test_freq_0.tsv": {"num_bytes": 1049105, "checksum": "e70ca7cacfd43b424fbd375ac8fc3e7bde24538caccdcbd19c056b8825437422"}, "./data/challenge_sets/disc_test_freq_1.tsv": {"num_bytes": 1055004, "checksum": "0f9ab3179e19ee7d48e9d4ffd81ea0d37f08ff17213d3e6c5c02a3a3d2cc4921"}, "./data/challenge_sets/disc_test_freq_2.tsv": {"num_bytes": 248090, "checksum": "3c912b019fa5d1f39c7d83e404f79250e6b54ef127aff201cab3faeac186995a"}, "./data/challenge_sets/disc_test_freq_3.tsv": {"num_bytes": 13074, "checksum": "e99097dd34607d0f6c03a17cb35125f37f15571d5dfe57d4218f4db56d45c40b"}}, "download_size": 4971305, "post_processing_size": null, "dataset_size": 9633857, "size_in_bytes": 14605162}}