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'''DiscEvalMT: DiscEvalMT: Contrastive test sets for the evaluation of discourse in machine translation (v2)''' |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = '''\ |
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@inproceedings{bawden-etal-2018-evaluating, |
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title = "Evaluating Discourse Phenomena in Neural Machine Translation", |
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author = "Bawden, Rachel and Sennrich, Rico and Birch, Alexandra and Haddow, Barry", |
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booktitle = {{Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)}}, |
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month = jun, |
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year = "2018", |
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address = "New Orleans, Louisiana", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/N18-1118", |
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doi = "10.18653/v1/N18-1118", |
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pages = "1304--1313" |
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} |
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''' |
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_DESCRIPTION = '''\ |
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English-French hand-crafted contrastive test set to test anaphora and lexical choice |
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''' |
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_HOMEPAGE='https://github.com/rbawden/discourse-mt-test-sets/tree/master' |
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_LICENSE = 'CC-BY-SA-4.0' |
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_URLS = { |
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'test-lexical_choice': 'lexical_choice/eval.jsonl', |
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'test-anaphora': 'anaphora/eval.jsonl' |
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} |
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class DiscEvalMTConfig(datasets.BuilderConfig): |
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'''BuilderConfig for DiscEvalMT.''' |
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def __init__(self, evaltype: str, **kwargs): |
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"""BuilderConfig for DiscEvalMT. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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self.evaltype = evaltype |
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if evaltype not in ['anaphora', 'lexical_choice']: |
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raise ValueError("Invalid evaltype: %s. You must choose between 'anaphora' and 'lexical_choice' " % evaltype) |
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super(DiscEvalMTConfig, self).__init__(**kwargs) |
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class DiscEvalMT(datasets.GeneratorBasedBuilder): |
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'''DiscEvalMT: English-French contrastive test set for 2 discourse phenomena (anaphora and lexical cohesion)''' |
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BUILDER_CONFIG_CLASS = DiscEvalMTConfig |
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BUILDER_CONFIGS = [ |
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DiscEvalMTConfig( |
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evaltype='anaphora', |
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name='anaphora', |
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version=datasets.Version('2.0.0'), |
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), |
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DiscEvalMTConfig( |
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evaltype='lexical_choice', |
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name='lexical_choice', |
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version=datasets.Version('2.0.0'), |
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), |
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] |
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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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"split": datasets.Value("string"), |
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"ex_num": datasets.Value("int64"), |
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"type": datasets.Value("string"), |
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"context_src": datasets.Value("string"), |
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"current_src": datasets.Value("string"), |
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"context_trg": datasets.Value("string"), |
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"current_trg": datasets.Value("string"), |
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"contrastive_context_trg": datasets.Value("string"), |
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"contrastive_current_trg": datasets.Value("string"), |
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"correct_or_semicorrect": datasets.Value("string")}), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return datasets.SplitGenerator(name="test", gen_kwargs={'filepath': downloaded_files['test-' + self.config.evaltype]}), |
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def _generate_examples(self, filepath): |
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logger.info("generating examples from = %s", filepath) |
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key = 0 |
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with open(filepath, encoding="utf-8") as f: |
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for i, line in enumerate(f): |
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example = json.loads(line) |
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print(example.keys()) |
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yield i, example |
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