wiki_auto_asset_turk.py

import csv
import json
import os
import datasets

_CITATION = """\
@inproceedings{jiang-etal-2020-neural,
    title = "Neural {CRF} Model for Sentence Alignment in Text Simplification",
    author = "Jiang, Chao  and
      Maddela, Mounica  and
      Lan, Wuwei  and
      Zhong, Yang  and
      Xu, Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.709",
    doi = "10.18653/v1/2020.acl-main.709",
    pages = "7943--7960",
}
"""

_DESCRIPTION = """\
WikiAuto provides a set of aligned sentences from English Wikipedia and Simple
English Wikipedia as a resource to train sentence simplification systems.

The authors first crowd-sourced a set of manual alignments between sentences in
a subset of the Simple English Wikipedia and their corresponding versions in
English Wikipedia (this corresponds to the manual config in this version of the
dataset), then trained a neural CRF system to predict these alignments.

The trained alignment prediction model was then applied to the other articles in
Simple English Wikipedia with an English counterpart to create a larger corpus
of aligned sentences (corresponding to the auto and auto_acl configs here).
"""

_URLs = {
    "train": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.tsv",
    "validation": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/valid.tsv",
    "test_turk": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_turk_detokenized.json",
    "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/wiki_auto_asset_turk_train_valid.zip",
}

# Add Asset files.
_URLs[
    "test_asset_orig"
] = "https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.orig"
for i in range(10):
    _URLs[
        f"test_asset_{i}"
    ] = f"https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.simp.{i}"


class E2ENlg(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    DEFAULT_CONFIG_NAME = "wiki_auto_asset_turk"

    def _info(self):
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "gem_parent_id": datasets.Value("string"),
                "source": datasets.Value("string"),
                "target": datasets.Value("string"),
                "references": [datasets.Value("string")],
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=datasets.info.SupervisedKeysData(
                input="source", output="target"
            ),
            homepage="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_dir = dl_manager.download_and_extract(_URLs)
        challenge_sets = [
            (
                "challenge_train_sample",
                "train_wiki_auto_asset_turk_RandomSample500.json",
            ),
            (
                "challenge_validation_sample",
                "validation_wiki_auto_asset_turk_RandomSample500.json",
            ),
            (
                "challenge_test_asset_backtranslation",
                "test_asset_wiki_auto_asset_turk_BackTranslation.json",
            ),
            (
                "challenge_test_asset_bfp02",
                "test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json",
            ),
            (
                "challenge_test_asset_bfp05",
                "test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json",
            ),
            (
                "challenge_test_asset_nopunc",
                "test_asset_wiki_auto_asset_turk_WithoutPunctuation.json",
            ),
            (
                "challenge_test_turk_backtranslation",
                "detok_test_turk_wiki_auto_asset_turk_BackTranslation.json",
            ),
            (
                "challenge_test_turk_bfp02",
                "detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json",
            ),
            (
                "challenge_test_turk_bfp05",
                "detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json",
            ),
            (
                "challenge_test_turk_nopunc",
                "detok_test_turk_wiki_auto_asset_turk_WithoutPunctuation.json",
            ),
        ]
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": dl_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": dl_dir["validation"],
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name="test_asset",
                gen_kwargs={
                    "filepath": "",
                    "split": "test_asset",
                    "filepaths": [dl_dir["test_asset_orig"]]
                    + [dl_dir[f"test_asset_{i}"] for i in range(10)],
                },
            ),
            datasets.SplitGenerator(
                name="test_turk",
                gen_kwargs={
                    "filepath": dl_dir["test_turk"],
                    "split": "test_turk",
                },
            ),
        ] + [
            datasets.SplitGenerator(
                name=challenge_split,
                gen_kwargs={
                    "filepath": os.path.join(
                        dl_dir["challenge_set"], "wiki_auto_asset_turk", filename
                    ),
                    "split": challenge_split,
                },
            )
            for challenge_split, filename in challenge_sets
        ]

    def _generate_examples(self, filepath, split, filepaths=None, lang=None):
        """Yields examples."""
        if split in ["train", "validation"]:
            keys = [
                "source",
                "target",
            ]
            with open(filepath, encoding="utf-8") as f:
                for id_, line in enumerate(f):
                    values = line.strip().split("\t")
                    assert (
                        len(values) == 2
                    ), f"Not enough fields in ---- {line} --- {values}"
                    example = dict([(k, val) for k, val in zip(keys, values)])
                    example["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}"
                    example["gem_parent_id"] = example["gem_id"]
                    example["references"] = (
                        [] if split == "train" else [example["target"]]
                    )
                    yield id_, example
        elif split == "test_turk":
            examples = json.load(open(filepath, encoding="utf-8"))
            for id_, example in enumerate(examples):
                example["gem_parent_id"] = example["gem_id"]
                for k in ["source_id", "target_id"]:
                    if k in example:
                        del example[k]
                yield id_, example
        elif split == "test_asset":
            files = [open(f_name, encoding="utf-8") for f_name in filepaths]
            for id_, lines in enumerate(zip(*files)):
                yield id_, {
                    "gem_id": f"wiki_auto_asset_turk-{split}-{id_}",
                    "gem_parent_id": f"wiki_auto_asset_turk-{split}-{id_}",
                    "target": lines[1].strip(),
                    "source": lines[0].strip(),
                    "references": [line.strip() for line in lines[1:]],
                }
        else:
            exples = json.load(open(filepath, encoding="utf-8"))
            if isinstance(exples, dict):
                assert len(exples) == 1, "multiple entries found"
                exples = list(exples.values())[0]
            for id_, exple in enumerate(exples):
                exple["gem_parent_id"] = exple["gem_id"]
                exple["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}"
                for k in ["source_id", "target_id"]:
                    if k in exple:
                        del exple[k]
                yield id_, exple