File size: 3,141 Bytes
403c49e 2eeba6b 403c49e 5ddf90b 403c49e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
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"]],
}
|