| import csv | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{juraska-etal-2019-viggo, | |
| title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation", | |
| author = "Juraska, Juraj and | |
| Bowden, Kevin and | |
| Walker, Marilyn", | |
| booktitle = "Proceedings of the 12th International Conference on Natural Language Generation", | |
| month = oct # "{--}" # nov, | |
| year = "2019", | |
| address = "Tokyo, Japan", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/W19-8623", | |
| doi = "10.18653/v1/W19-8623", | |
| pages = "164--172", | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models. | |
| """ | |
| _URLs = { | |
| "train": "train.csv", | |
| "validation": "validation.csv", | |
| "test": "test.csv", | |
| "challenge_train_1_percent": "challenge_train_1_percent.csv", | |
| "challenge_train_2_percent": "challenge_train_2_percent.csv", | |
| "challenge_train_5_percent": "challenge_train_5_percent.csv", | |
| "challenge_train_10_percent": "challenge_train_10_percent.csv", | |
| "challenge_train_20_percent": "challenge_train_20_percent.csv", | |
| } | |
| class Viggo(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| DEFAULT_CONFIG_NAME = "viggo" | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "gem_id": datasets.Value("string"), | |
| "meaning_representation": datasets.Value("string"), | |
| "target": datasets.Value("string"), | |
| "references": [datasets.Value("string")], | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=datasets.info.SupervisedKeysData( | |
| input="meaning_representation", output="target" | |
| ), | |
| homepage="https://nlds.soe.ucsc.edu/viggo", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| dl_dir = dl_manager.download_and_extract(_URLs) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl} | |
| ) | |
| for spl in _URLs.keys() | |
| ] | |
| def _generate_examples(self, filepath, split, filepaths=None, lang=None): | |
| """Yields examples.""" | |
| with open(filepath, "r", encoding='utf-8-sig') as csvfile: | |
| reader = csv.DictReader(csvfile) | |
| for id_, row in enumerate(reader): | |
| yield id_, { | |
| "gem_id": f"viggo-{split}-{id_}", | |
| "meaning_representation": row["mr"], | |
| "target": row["ref"], | |
| "references": [row["ref"]], | |
| } | |