Create script

challenge-2023.py

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+# 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