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import csv |
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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@inproceedings{jiang-etal-2020-neural, |
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title = "Neural {CRF} Model for Sentence Alignment in Text Simplification", |
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author = "Jiang, Chao and |
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Maddela, Mounica and |
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Lan, Wuwei and |
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Zhong, Yang and |
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Xu, Wei", |
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booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", |
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month = jul, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.acl-main.709", |
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doi = "10.18653/v1/2020.acl-main.709", |
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pages = "7943--7960", |
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} |
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""" |
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_DESCRIPTION = """\ |
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WikiAuto provides a set of aligned sentences from English Wikipedia and Simple |
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English Wikipedia as a resource to train sentence simplification systems. |
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The authors first crowd-sourced a set of manual alignments between sentences in |
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a subset of the Simple English Wikipedia and their corresponding versions in |
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English Wikipedia (this corresponds to the manual config in this version of the |
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dataset), then trained a neural CRF system to predict these alignments. |
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The trained alignment prediction model was then applied to the other articles in |
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Simple English Wikipedia with an English counterpart to create a larger corpus |
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of aligned sentences (corresponding to the auto and auto_acl configs here). |
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""" |
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_URLs = { |
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"train": "train.tsv", |
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"validation": "valid.tsv", |
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"test_turk": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_turk_detokenized.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/wiki_auto_asset_turk_train_valid.zip", |
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"test_contract": "benchmarks/contract-benchmark.tsv", |
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"test_wiki": "benchmarks/wiki-benchmark.tsv", |
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} |
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_URLs[ |
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"test_asset_orig" |
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] = "https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.orig" |
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for i in range(10): |
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_URLs[ |
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f"test_asset_{i}" |
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] = f"https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.simp.{i}" |
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class WikiAuto(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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DEFAULT_CONFIG_NAME = "wiki_auto_asset_turk" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
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"gem_parent_id": datasets.Value("string"), |
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"source": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=datasets.info.SupervisedKeysData( |
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input="source", output="target" |
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), |
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homepage="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_dir = dl_manager.download_and_extract(_URLs) |
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challenge_sets = [ |
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( |
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"challenge_train_sample", |
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"train_wiki_auto_asset_turk_RandomSample500.json", |
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), |
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( |
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"challenge_validation_sample", |
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"validation_wiki_auto_asset_turk_RandomSample500.json", |
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), |
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( |
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"challenge_test_asset_backtranslation", |
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"test_asset_wiki_auto_asset_turk_BackTranslation.json", |
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), |
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( |
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"challenge_test_asset_bfp02", |
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"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json", |
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), |
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( |
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"challenge_test_asset_bfp05", |
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"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json", |
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), |
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( |
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"challenge_test_asset_nopunc", |
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"test_asset_wiki_auto_asset_turk_WithoutPunctuation.json", |
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), |
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( |
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"challenge_test_turk_backtranslation", |
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"detok_test_turk_wiki_auto_asset_turk_BackTranslation.json", |
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), |
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( |
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"challenge_test_turk_bfp02", |
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"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json", |
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), |
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( |
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"challenge_test_turk_bfp05", |
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"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json", |
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), |
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( |
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"challenge_test_turk_nopunc", |
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"detok_test_turk_wiki_auto_asset_turk_WithoutPunctuation.json", |
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), |
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] |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": dl_dir["train"], |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": dl_dir["validation"], |
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"split": "validation", |
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}, |
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), |
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datasets.SplitGenerator( |
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name="test_asset", |
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gen_kwargs={ |
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"filepath": "", |
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"split": "test_asset", |
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"filepaths": [dl_dir["test_asset_orig"]] |
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+ [dl_dir[f"test_asset_{i}"] for i in range(10)], |
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}, |
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), |
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datasets.SplitGenerator( |
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name="test_turk", |
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gen_kwargs={ |
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"filepath": dl_dir["test_turk"], |
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"split": "test_turk", |
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}, |
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), |
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datasets.SplitGenerator( |
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name="test_contract", |
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gen_kwargs={ |
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"filepath": dl_dir["test_contract"], |
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"split": "test_contract", |
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}, |
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), |
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datasets.SplitGenerator( |
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name="test_wiki", |
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gen_kwargs={ |
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"filepath": dl_dir["test_wiki"], |
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"split": "test_wiki", |
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}, |
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), |
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] + [ |
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datasets.SplitGenerator( |
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name=challenge_split, |
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gen_kwargs={ |
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"filepath": os.path.join( |
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dl_dir["challenge_set"], "wiki_auto_asset_turk", filename |
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), |
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"split": challenge_split, |
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}, |
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) |
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for challenge_split, filename in challenge_sets |
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] |
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def _generate_examples(self, filepath, split, filepaths=None, lang=None): |
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"""Yields examples.""" |
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if split in ["train", "validation"]: |
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keys = [ |
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"source", |
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"target", |
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] |
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with open(filepath, encoding="utf-8") as f: |
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for id_, line in enumerate(f): |
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values = line.strip().split("\t") |
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assert ( |
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len(values) == 2 |
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), f"Not enough fields in ---- {line} --- {values}" |
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example = dict([(k, val) for k, val in zip(keys, values)]) |
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example["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}" |
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example["gem_parent_id"] = example["gem_id"] |
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example["references"] = ( |
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[] if split == "train" else [example["target"]] |
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) |
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yield id_, example |
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elif split == "test_turk": |
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examples = json.load(open(filepath, encoding="utf-8")) |
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for id_, example in enumerate(examples): |
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example["gem_parent_id"] = example["gem_id"] |
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for k in ["source_id", "target_id"]: |
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if k in example: |
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del example[k] |
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yield id_, example |
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elif split == "test_asset": |
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files = [open(f_name, encoding="utf-8") for f_name in filepaths] |
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for id_, lines in enumerate(zip(*files)): |
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yield id_, { |
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"gem_id": f"wiki_auto_asset_turk-{split}-{id_}", |
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"gem_parent_id": f"wiki_auto_asset_turk-{split}-{id_}", |
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"target": lines[1].strip(), |
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"source": lines[0].strip(), |
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"references": [line.strip() for line in lines[1:]], |
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} |
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elif split == "test_wiki" or split == "test_contract": |
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with open(filepath, 'r') as f: |
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reader = csv.DictReader(f, delimiter="\t") |
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for id_, entry in enumerate(reader): |
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yield id_, { |
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"gem_id": f"wiki_auto_asset_turk-{split}-{id_}", |
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"gem_parent_id": f"wiki_auto_asset_turk-{split}-{id_}", |
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"target": entry["simple"], |
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"source": entry["complex"], |
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"references": [entry["simple"]], |
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} |
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else: |
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exples = json.load(open(filepath, encoding="utf-8")) |
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if isinstance(exples, dict): |
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assert len(exples) == 1, "multiple entries found" |
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exples = list(exples.values())[0] |
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for id_, exple in enumerate(exples): |
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exple["gem_parent_id"] = exple["gem_id"] |
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exple["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}" |
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for k in ["source_id", "target_id"]: |
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if k in exple: |
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del exple[k] |
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yield id_, exple |
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