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"""JFLEG dataset.""" |
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import datasets |
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_CITATION = """\ |
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@InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort, |
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author = {Napoles, Courtney |
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and Sakaguchi, Keisuke |
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and Tetreault, Joel}, |
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title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction}, |
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booktitle = {Proceedings of the 15th Conference of the European Chapter of the |
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Association for Computational Linguistics: Volume 2, Short Papers}, |
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month = {April}, |
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year = {2017}, |
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address = {Valencia, Spain}, |
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publisher = {Association for Computational Linguistics}, |
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pages = {229--234}, |
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url = {http://www.aclweb.org/anthology/E17-2037} |
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} |
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@InProceedings{heilman-EtAl:2014:P14-2, |
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author = {Heilman, Michael |
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and Cahill, Aoife |
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and Madnani, Nitin |
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and Lopez, Melissa |
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and Mulholland, Matthew |
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and Tetreault, Joel}, |
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title = {Predicting Grammaticality on an Ordinal Scale}, |
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booktitle = {Proceedings of the 52nd Annual Meeting of the |
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Association for Computational Linguistics (Volume 2: Short Papers)}, |
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month = {June}, |
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year = {2014}, |
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address = {Baltimore, Maryland}, |
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publisher = {Association for Computational Linguistics}, |
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pages = {174--180}, |
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url = {http://www.aclweb.org/anthology/P14-2029} |
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} |
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""" |
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_DESCRIPTION = """\ |
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JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. |
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It is a gold standard benchmark for developing and evaluating GEC systems with respect to |
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fluency (extent to which a text is native-sounding) as well as grammaticality. |
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For each source document, there are four human-written corrections (ref0 to ref3). |
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""" |
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_HOMEPAGE = "https://github.com/keisks/jfleg" |
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_LICENSE = "CC BY-NC-SA 4.0" |
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_URLs = { |
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"dev": { |
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"src": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.src", |
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"ref0": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref0", |
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"ref1": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref1", |
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"ref2": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref2", |
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"ref3": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref3", |
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}, |
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"test": { |
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"src": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.src", |
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"ref0": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref0", |
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"ref1": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref1", |
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"ref2": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref2", |
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"ref3": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref3", |
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}, |
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} |
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class Jfleg(datasets.GeneratorBasedBuilder): |
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"""JFLEG (JHU FLuency-Extended GUG) grammatical error correction dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{"sentence": datasets.Value("string"), "corrections": datasets.Sequence(datasets.Value("string"))} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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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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downloaded_dev = dl_manager.download_and_extract(_URLs["dev"]) |
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downloaded_test = dl_manager.download_and_extract(_URLs["test"]) |
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return [ |
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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": downloaded_dev, |
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"split": "dev", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": downloaded_test, "split": "test"}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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"""Yields examples.""" |
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source_file = filepath["src"] |
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with open(source_file, encoding="utf-8") as f: |
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source_sentences = f.read().split("\n") |
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num_source = len(source_sentences) |
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corrections = [] |
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for n in range(0, 4): |
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correction_file = filepath["ref{n}".format(n=n)] |
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with open(correction_file, encoding="utf-8") as f: |
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correction_sentences = f.read().split("\n") |
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num_correction = len(correction_sentences) |
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assert len(correction_sentences) == len( |
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source_sentences |
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), "Sizes do not match: {ns} vs {nr} for {sf} vs {cf}.".format( |
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ns=num_source, nr=num_correction, sf=source_file, cf=correction_file |
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) |
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corrections.append(correction_sentences) |
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corrected_sentences = list(zip(*corrections)) |
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for id_, source_sentence in enumerate(source_sentences): |
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yield id_, {"sentence": source_sentence, "corrections": corrected_sentences[id_]} |
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