Datasets:
Tasks:
Tabular to Text
Sub-tasks:
rdf-to-text
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
found
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""The WebNLG 2023 Challenge.""" | |
import os | |
import xml.etree.ElementTree as ET | |
from collections import defaultdict | |
import datasets | |
_HOMEPAGE = "https://synalp.gitlabpages.inria.fr/webnlg-challenge/challenge_2023/" | |
_DESCRIPTION = """\ | |
The WebNLG challenge consists in mapping data to text. The training data consists | |
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation | |
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b). | |
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot) | |
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot | |
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation | |
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties), | |
aggregation (how to avoid repetitions) and surface realisation | |
(how to build a syntactically correct and natural sounding text). | |
""" | |
_LICENSE = "" | |
_CITATION = """\ | |
@inproceedings{web_nlg, | |
author = {Claire Gardent and | |
Anastasia Shimorina and | |
Shashi Narayan and | |
Laura Perez{-}Beltrachini}, | |
editor = {Regina Barzilay and | |
Min{-}Yen Kan}, | |
title = {Creating Training Corpora for {NLG} Micro-Planners}, | |
booktitle = {Proceedings of the 55th Annual Meeting of the | |
Association for Computational Linguistics, | |
{ACL} 2017, Vancouver, Canada, July 30 - August 4, | |
Volume 1: Long Papers}, | |
pages = {179--188}, | |
publisher = {Association for Computational Linguistics}, | |
year = {2017}, | |
url = {https://doi.org/10.18653/v1/P17-1017}, | |
doi = {10.18653/v1/P17-1017} | |
} | |
""" | |
# From: https://github.com/WebNLG/2023-Challenge | |
_URL = "data.zip" | |
_LANGUAGES = ["br", "cy", "ga", "mt", "ru"] | |
def et_to_dict(tree): | |
dct = {tree.tag: {} if tree.attrib else None} | |
children = list(tree) | |
if children: | |
dd = defaultdict(list) | |
for dc in map(et_to_dict, children): | |
for k, v in dc.items(): | |
dd[k].append(v) | |
dct = {tree.tag: dd} | |
if tree.attrib: | |
dct[tree.tag].update((k, v) for k, v in tree.attrib.items()) | |
if tree.text: | |
text = tree.text.strip() | |
if children or tree.attrib: | |
if text: | |
dct[tree.tag]["text"] = text | |
else: | |
dct[tree.tag] = text | |
return dct | |
def parse_entry(entry): | |
res = {} | |
otriple_set_list = entry["originaltripleset"] | |
res["original_triple_sets"] = [{"otriple_set": otriple_set["otriple"]} for otriple_set in otriple_set_list] | |
mtriple_set_list = entry["modifiedtripleset"] | |
res["modified_triple_sets"] = [{"mtriple_set": mtriple_set["mtriple"]} for mtriple_set in mtriple_set_list] | |
res["category"] = entry["category"] | |
res["eid"] = entry["eid"] | |
res["size"] = int(entry["size"]) | |
res["lex"] = { | |
"comment": [ex.get("comment", "") for ex in entry.get("lex", [])], | |
"lid": [ex.get("lid", "") for ex in entry.get("lex", [])], | |
"text": [ex.get("text", "") for ex in entry.get("lex", [])], | |
"lang": [ex.get("lang", "") for ex in entry.get("lex", [])], | |
} | |
res["shape"] = entry.get("shape", "") | |
res["shape_type"] = entry.get("shape_type", "") | |
return res | |
def xml_file_to_examples(filename): | |
tree = ET.parse(filename).getroot() | |
examples = et_to_dict(tree)["benchmark"]["entries"][0]["entry"] | |
return [parse_entry(entry) for entry in examples] | |
class Challenge2023(datasets.GeneratorBasedBuilder): | |
"""The WebNLG 2023 Challenge dataset.""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [datasets.BuilderConfig(name=language) for language in _LANGUAGES] | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"category": datasets.Value("string"), | |
"size": datasets.Value("int32"), | |
"eid": datasets.Value("string"), | |
"original_triple_sets": datasets.Sequence( | |
{"otriple_set": datasets.Sequence(datasets.Value("string"))} | |
), | |
"modified_triple_sets": datasets.Sequence( | |
{"mtriple_set": datasets.Sequence(datasets.Value("string"))} | |
), | |
"shape": datasets.Value("string"), | |
"shape_type": datasets.Value("string"), | |
"lex": datasets.Sequence( | |
{ | |
"comment": datasets.Value("string"), | |
"lid": datasets.Value("string"), | |
"text": datasets.Value("string"), | |
"lang": datasets.Value("string"), | |
} | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_dir = dl_manager.download_and_extract(_URL) | |
splits = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev"} | |
return [ | |
datasets.SplitGenerator( | |
name=split, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"xml_file": os.path.join(data_dir, "data", f"{self.config.name}_{split_filename}.xml"), | |
}, | |
) | |
for split, split_filename in splits.items() | |
] | |
def _generate_examples(self, xml_file): | |
"""Yields examples.""" | |
id_ = 0 | |
for exple_dict in xml_file_to_examples(xml_file): | |
yield id_, exple_dict | |
id_ += 1 | |