Added loading script

wmt_vat.py

@@ -0,0 +1,152 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Variance-Aware Machine Translation Test Sets"""
+
+import os
+import json
+import textwrap
+from typing import List
+
+import datasets
+from datasets.utils.download_manager import DownloadManager
+
+
+_CITATION = """\
+@inproceedings{
+    zhan2021varianceaware,
+    title={Variance-Aware Machine Translation Test Sets},
+    author={Runzhe Zhan and Xuebo Liu and Derek F. Wong and Lidia S. Chao},
+    booktitle={Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track},
+    year={2021},
+    url={https://openreview.net/forum?id=hhKA5k0oVy5}
+}
+"""
+
+_DESCRIPTION = """\
+The Variance-Aware Machine Translation corpus contains 70 small and discriminative test sets for machine translation (MT) 
+evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions. 
+VAT is automatically created by a novel variance-aware filtering method that filters the indiscriminative test instances 
+of the current MT benchmark without any human labor. Experimental results show that VAT outperforms the original WMT benchmark 
+in terms of the correlation with human judgment across mainstream language pairs and test sets. Further analysis on the properties 
+of VAT reveals the challenging linguistic features (e.g., translation of low-frequency words and proper nouns) for the competitive 
+MT systems, providing guidance for constructing future MT test sets. 
+"""
+
+_HOMEPAGE = "https://github.com/NLP2CT/Variance-Aware-MT-Test-Sets"
+
+_LICENSE = "https://raw.githubusercontent.com/NLP2CT/Variance-Aware-MT-Test-Sets/main/LICENSE"
+
+_BASE_URL = "https://github.com/NLP2CT/Variance-Aware-MT-Test-Sets/VAT_data"
+_META_URL = "https://github.com/NLP2CT/Variance-Aware-MT-Test-Sets/VAT_meta"
+
+_CONFIGS = {
+    "wmt16": ["tr_en", "ru_en", "ro_en", "de_en", "en_ru", "fi_en", "cs_en"],
+    "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"],
+    "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"],
+    "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"],
+    "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"],
+}
+
+_PATHS = {
+    f"{year}_{pair}": {
+        "src" : os.path.join(_BASE_URL, year, f"vat_newstest20{year[3:]}-{pair.replace('_', '-')}-src.{pair.split('_')[0]}"),
+        "ref" : os.path.join(_BASE_URL, year, f"vat_newstest20{year[3:]}-{pair.replace('_', '-')}-ref.{pair.split('_')[1]}")
+    } for year, pairs in _CONFIGS.items() for pair in pairs
+}
+
+_METADATA_PATHS = {k:os.path.join(_META_URL, k, "bert-r_filter-std60.json") for k in _CONFIGS.keys()}
+
+
+class WmtVatConfig(datasets.BuilderConfig):
+    def __init__(
+        self,
+        campaign: str,
+        source: str, 
+        reference: str,
+        **kwargs
+    ):
+        """BuilderConfig for Variance-Aware MT Test Sets.
+
+        Args:
+            campaign: `str`, WMT campaign from which the test set was extracted
+            source: `str`, source for translation.
+            reference: `str`, reference translation.
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+        self.campaign = campaign
+        self.source = source
+        self.reference = reference
+
+
+class WmtVat(datasets.GeneratorBasedBuilder):
+    """Variance-Aware Machine Translation Test Sets"""
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        WmtVatConfig(
+            name=cfg,
+            campaign=cfg.split("_")[0],
+            source=cfg.split("_")[1],
+            reference=cfg.split("_")[2],
+        ) for cfg in _PATHS.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "orig_id": datasets.Value("int32"),
+                "source": datasets.Value("string"),
+                "reference": datasets.Value("string")
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        """Returns SplitGenerators."""
+        src_file = dl_manager.download_and_extract(_PATHS[self.config.name]["src"])
+        ref_file = dl_manager.download_and_extract(_PATHS[self.config.name]["ref"])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "src_path": src_file,
+                    "ref_path": ref_file,
+                    "pair": self.config.name[5:].replace("_", "-"),
+                    "meta_path": _METADATA_PATHS[self.config.name[:5]] # Only wmtXX
+                },
+            )
+        ]
+
+    def _generate_examples(
+        self, src_path: str, ref_path: str, pair: str, meta_path: str
+    ):
+        """ Yields examples as (key, example) tuples. """
+        with open(meta_path, encoding="utf-8") as meta:
+            ids = json.load(meta)[pair]
+        with open(src_path, encoding="utf-8") as src:
+            with open(src_path, encoding="utf-8") as ref:
+                for id_, (src_ex, ref_ex, orig_idx) in enumerate(zip(src, ref, ids)):
+                    yield id_, {
+                        "orig_id": orig_idx,
+                        "source": src_ex,
+                        "reference": ref_ex,
+                    }