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"""Variance-Aware Machine Translation Test Sets""" |
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import os |
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import json |
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import textwrap |
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from typing import List |
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import datasets |
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from datasets.utils.download_manager import DownloadManager |
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_CITATION = """\ |
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@inproceedings{ |
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zhan2021varianceaware, |
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title={Variance-Aware Machine Translation Test Sets}, |
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author={Runzhe Zhan and Xuebo Liu and Derek F. Wong and Lidia S. Chao}, |
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booktitle={Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track}, |
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year={2021}, |
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url={https://openreview.net/forum?id=hhKA5k0oVy5} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The Variance-Aware Machine Translation corpus contains 70 small and discriminative test sets for machine translation (MT) |
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evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions. |
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VAT is automatically created by a novel variance-aware filtering method that filters the indiscriminative test instances |
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of the current MT benchmark without any human labor. Experimental results show that VAT outperforms the original WMT benchmark |
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in terms of the correlation with human judgment across mainstream language pairs and test sets. Further analysis on the properties |
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of VAT reveals the challenging linguistic features (e.g., translation of low-frequency words and proper nouns) for the competitive |
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MT systems, providing guidance for constructing future MT test sets. |
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""" |
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_HOMEPAGE = "https://github.com/NLP2CT/Variance-Aware-MT-Test-Sets" |
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_LICENSE = "https://raw.githubusercontent.com/NLP2CT/Variance-Aware-MT-Test-Sets/main/LICENSE" |
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_BASE_URL = "https://github.com/NLP2CT/Variance-Aware-MT-Test-Sets/raw/main/VAT_data" |
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_META_URL = "https://raw.githubusercontent.com/NLP2CT/Variance-Aware-MT-Test-Sets/main/VAT_meta" |
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_CONFIGS = { |
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"wmt16": ["tr_en", "ru_en", "ro_en", "de_en", "en_ru", "fi_en", "cs_en"], |
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"wmt17": ["en_lv", "zh_en", "en_tr", "lv_en", "en_de", "ru_en", "en_fi", "tr_en", "en_zh", "en_ru", "fi_en", "en_cs", "de_en", "cs_en"], |
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"wmt18": ["en_cs", "cs_en", "en_fi", "en_tr", "en_et", "ru_en", "et_en", "tr_en", "fi_en", "zh_en", "en_zh", "en_ru", "de_en", "en_de"], |
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"wmt19": ["zh_en", "en_cs", "de_en", "en_gu", "fr_de", "en_zh", "fi_en", "en_fi", "kk_en", "de_cs", "lt_en", "en_lt", "ru_en", "en_kk", "en_ru", "gu_en", "de_fr", "en_de"], |
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"wmt20": ["km_en", "cs_en", "en_de", "ja_en", "ps_en", "en_zh", "en_ta", "de_en", "zh_en", "en_ja", "en_cs", "en_pl", "en_ru", "pl_en", "iu_en", "ru_en", "ta_en"], |
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} |
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_PATHS = { |
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f"{year}_{pair}": { |
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"src" : os.path.join(_BASE_URL, year, f"vat_newstest20{year[3:]}-{pair.replace('_', '')}-src.{pair.split('_')[0]}{'.txt' if year == 'wmt20' else ''}"), |
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"ref" : os.path.join(_BASE_URL, year, f"vat_newstest20{year[3:]}-{pair.replace('_', '')}-ref.{pair.split('_')[1]}{'.txt' if year == 'wmt20' else ''}") |
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} for year, pairs in _CONFIGS.items() for pair in pairs |
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} |
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_METADATA_PATHS = {k:os.path.join(_META_URL, k, "bert-r_filter-std60.json") for k in _CONFIGS.keys()} |
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class WmtVatConfig(datasets.BuilderConfig): |
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def __init__( |
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self, |
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campaign: str, |
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source: str, |
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reference: str, |
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**kwargs |
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): |
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"""BuilderConfig for Variance-Aware MT Test Sets. |
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Args: |
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campaign: `str`, WMT campaign from which the test set was extracted |
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source: `str`, source for translation. |
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reference: `str`, reference translation. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(**kwargs) |
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self.campaign = campaign |
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self.source = source |
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self.reference = reference |
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class WmtVat(datasets.GeneratorBasedBuilder): |
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"""Variance-Aware Machine Translation Test Sets""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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WmtVatConfig( |
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name=cfg, |
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campaign=cfg.split("_")[0], |
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source=cfg.split("_")[1], |
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reference=cfg.split("_")[2], |
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) for cfg in _PATHS.keys() |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"orig_id": datasets.Value("int32"), |
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"source": datasets.Value("string"), |
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"reference": 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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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: DownloadManager): |
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"""Returns SplitGenerators.""" |
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src_file = dl_manager.download_and_extract(_PATHS[self.config.name]["src"]) |
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ref_file = dl_manager.download_and_extract(_PATHS[self.config.name]["ref"]) |
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meta_file = dl_manager.download_and_extract(_METADATA_PATHS[self.config.name[:5]]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"src_path": src_file, |
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"ref_path": ref_file, |
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"pair": self.config.name[6:].replace("_", "-"), |
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"meta_path": meta_file |
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}, |
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) |
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] |
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def _generate_examples( |
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self, src_path: str, ref_path: str, pair: str, meta_path: str |
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): |
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""" Yields examples as (key, example) tuples. """ |
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with open(meta_path, encoding="utf-8") as meta: |
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ids = json.load(meta)[pair] |
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with open(src_path, encoding="utf-8") as src: |
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with open(ref_path, encoding="utf-8") as ref: |
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for id_, (src_ex, ref_ex, orig_idx) in enumerate(zip(src, ref, ids)): |
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yield id_, { |
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"orig_id": orig_idx, |
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"source": src_ex.strip(), |
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"reference": ref_ex.strip(), |
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} |