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americasnlp-mt-21.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""AmericasNLP 2021 Shared Task on Open Machine Translation."""
+
+import datasets
+
+from .wmt_utils import Wmt, WmtConfig
+
+
+_URL = "https://turing.iimas.unam.mx/americasnlp/2021/st.html"
+_CITATION = """
+@inproceedings{mager-etal-2021-findings,
+    title = "Findings of the {A}mericas{NLP} 2021 Shared Task on Open Machine Translation for Indigenous Languages of the {A}mericas",
+    author = "Mager, Manuel  and
+      Oncevay, Arturo  and
+      Ebrahimi, Abteen  and
+      Ortega, John  and
+      Rios, Annette  and
+      Fan, Angela  and
+      Gutierrez-Vasques, Ximena  and
+      Chiruzzo, Luis  and
+      Gim{\'e}nez-Lugo, Gustavo  and
+      Ramos, Ricardo  and
+      Meza Ruiz, Ivan Vladimir  and
+      Coto-Solano, Rolando  and
+      Palmer, Alexis  and
+      Mager-Hois, Elisabeth  and
+      Chaudhary, Vishrav  and
+      Neubig, Graham  and
+      Vu, Ngoc Thang  and
+      Kann, Katharina",
+    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas",
+    month = jun,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.americasnlp-1.23",
+    doi = "10.18653/v1/2021.americasnlp-1.23",
+    pages = "202--217",
+    abstract = "This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.",
+}
+"""
+
+_LANGUAGE_PAIRS = [(lang, "es") for lang in ["aym", "bzd", "cni", "gn", "nah", "oto", "quy", "shp"]]
+
+
+class AmericasNLPMT21(Wmt):
+    """AmericasNLP translation datasets for all {xx, "es"} language pairs."""
+
+    BUILDER_CONFIGS = [
+        WmtConfig(  # pylint:disable=g-complex-comprehension
+            description="AmericasNLP 2021 %s-%s translation task dataset." % (l1, l2),
+            url=_URL,
+            citation=_CITATION,
+            language_pair=(l1, l2),
+            version=datasets.Version("1.0.0"),
+        )
+        for l1, l2 in _LANGUAGE_PAIRS
+    ]
+
+    @property
+    def _subsets(self):
+        return {
+            datasets.Split.TRAIN: [
+                "americasnlp2021",
+            ],
+            datasets.Split.VALIDATION: ["dev2021"],
+            datasets.Split.TEST: ["test2021"],
+        }

data/americasnlp2021.zip

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+oid sha256:c781d22a46fc51f0158200817032cb8f9078a0ac4b1eaafa8602b7a7e508d369
+size 14309429

data/test2021.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e75bd6a8c60fecbf6b12b2b02d69e60153daadcd407c086279f15d6e927d4a2
+size 222057

wmt_utils.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+#
+# Script borrewed and adapted from https://huggingface.co/datasets/wmt16.
+
+# Lint as: python3
+"""WMT: Translate dataset."""
+
+
+import codecs
+import functools
+import glob
+import gzip
+import itertools
+import os
+import re
+import xml.etree.cElementTree as ElementTree
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_DESCRIPTION = """\
+Translation dataset based on the data from statmt.org.
+
+Versions exist for different years using a combination of data
+sources. The base `wmt` allows you to create a custom dataset by choosing
+your own data/language pair. This can be done as follows:
+
+```python
+from datasets import inspect_dataset, load_dataset_builder
+
+inspect_dataset("wmt16", "path/to/scripts")
+builder = load_dataset_builder(
+    "path/to/scripts/wmt_utils.py",
+    language_pair=("fr", "de"),
+    subsets={
+        datasets.Split.TRAIN: ["commoncrawl_frde"],
+        datasets.Split.VALIDATION: ["euelections_dev2019"],
+    },
+)
+
+# Standard version
+builder.download_and_prepare()
+ds = builder.as_dataset()
+
+# Streamable version
+ds = builder.as_streaming_dataset()
+```
+
+"""
+
+
+class SubDataset:
+    """Class to keep track of information on a sub-dataset of WMT."""
+
+    def __init__(self, name, target, sources, url, path, manual_dl_files=None):
+        """Sub-dataset of WMT.
+
+        Args:
+          name: `string`, a unique dataset identifier.
+          target: `string`, the target language code.
+          sources: `set<string>`, the set of source language codes.
+          url: `string` or `(string, string)`, URL(s) or URL template(s) specifying
+            where to download the raw data from. If two strings are provided, the
+            first is used for the source language and the second for the target.
+            Template strings can either contain '{src}' placeholders that will be
+            filled in with the source language code, '{0}' and '{1}' placeholders
+            that will be filled in with the source and target language codes in
+            alphabetical order, or all 3.
+          path: `string` or `(string, string)`, path(s) or path template(s)
+            specifing the path to the raw data relative to the root of the
+            downloaded archive. If two strings are provided, the dataset is assumed
+            to be made up of parallel text files, the first being the source and the
+            second the target. If one string is provided, both languages are assumed
+            to be stored within the same file and the extension is used to determine
+            how to parse it. Template strings should be formatted the same as in
+            `url`.
+          manual_dl_files: `<list>(string)` (optional), the list of files that must
+            be manually downloaded to the data directory.
+        """
+        self._paths = (path,) if isinstance(path, str) else path
+        self._urls = (url,) if isinstance(url, str) else url
+        self._manual_dl_files = manual_dl_files if manual_dl_files else []
+        self.name = name
+        self.target = target
+        self.sources = set(sources)
+
+    def _inject_language(self, src, strings):
+        """Injects languages into (potentially) template strings."""
+        if src not in self.sources:
+            raise ValueError(f"Invalid source for '{self.name}': {src}")
+
+        def _format_string(s):
+            if "{0}" in s and "{1}" and "{src}" in s:
+                return s.format(*sorted([src, self.target]), src=src)
+            elif "{0}" in s and "{1}" in s:
+                return s.format(*sorted([src, self.target]))
+            elif "{src}" in s:
+                return s.format(src=src)
+            else:
+                return s
+
+        return [_format_string(s) for s in strings]
+
+    def get_url(self, src):
+        return self._inject_language(src, self._urls)
+
+    def get_manual_dl_files(self, src):
+        return self._inject_language(src, self._manual_dl_files)
+
+    def get_path(self, src):
+        return self._inject_language(src, self._paths)
+
+
+# Subsets used in the training sets for AmericasNLP 2021 Shared Task on Machine Translation.
+_TRAIN_SUBSETS = [
+    SubDataset(
+        name="americasnlp2021",
+        target="es",
+        sources={"aym", "bzd", "cni", "gn", "nah", "oto", "quy", "shp"},
+        url="https://huggingface.co/datasets/vgaraujov/americasnlp-mt-21/resolve/main/data/americasnlp2021.zip",
+        path=("train.{src}-es.{src}", "train.{src}-es.es"),
+    ),
+]
+
+_DEV_SUBSETS = [
+    SubDataset(
+        name="dev2021",
+        target="es",
+        sources={"aym", "bzd", "cni", "gn", "nah", "oto", "quy", "shp"},
+        url="https://huggingface.co/datasets/vgaraujov/americasnlp-mt-21/resolve/main/data/americasnlp2021.zip",
+        path=("dev.{src}-es.{src}", "dev.{src}-es.es"),
+    ),
+    SubDataset(
+        name="test2021",
+        target="es",
+        sources={"aym", "bzd", "cni", "gn", "nah", "oto", "quy", "shp"},
+        url="https://huggingface.co/datasets/vgaraujov/americasnlp-mt-21/resolve/main/data/test2021.zip",
+        path=("test.{src}", "test.es"),
+    ),
+]
+
+DATASET_MAP = {dataset.name: dataset for dataset in _TRAIN_SUBSETS + _DEV_SUBSETS}
+
+
+class WmtConfig(datasets.BuilderConfig):
+    """BuilderConfig for WMT."""
+
+    def __init__(self, url=None, citation=None, description=None, language_pair=(None, None), subsets=None, **kwargs):
+        """BuilderConfig for WMT.
+
+        Args:
+          url: The reference URL for the dataset.
+          citation: The paper citation for the dataset.
+          description: The description of the dataset.
+          language_pair: pair of languages that will be used for translation. Should
+                     contain 2 letter coded strings. For example: ("en", "de").
+            configuration for the `datasets.features.text.TextEncoder` used for the
+            `datasets.features.text.Translation` features.
+          subsets: Dict[split, list[str]]. List of the subset to use for each of the
+            split. Note that WMT subclasses overwrite this parameter.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        name = "%s-%s" % (language_pair[0], language_pair[1])
+        if "name" in kwargs:  # Add name suffix for custom configs
+            name += "." + kwargs.pop("name")
+
+        super(WmtConfig, self).__init__(name=name, description=description, **kwargs)
+
+        self.url = url or "http://www.statmt.org"
+        self.citation = citation
+        self.language_pair = language_pair
+        self.subsets = subsets
+
+        # TODO(PVP): remove when manual dir works
+        # +++++++++++++++++++++
+        if language_pair[1] in ["cs", "hi", "ru"]:
+            assert NotImplementedError(f"The dataset for {language_pair[1]}-en is currently not fully supported.")
+        # +++++++++++++++++++++
+
+
+class Wmt(datasets.GeneratorBasedBuilder):
+    """WMT translation dataset."""
+
+    BUILDER_CONFIG_CLASS = WmtConfig
+
+    def __init__(self, *args, **kwargs):
+        super(Wmt, self).__init__(*args, **kwargs)
+
+    @property
+    def _subsets(self):
+        """Subsets that make up each split of the dataset."""
+        raise NotImplementedError("This is a abstract method")
+
+    @property
+    def subsets(self):
+        """Subsets that make up each split of the dataset for the language pair."""
+        source, target = self.config.language_pair
+        filtered_subsets = {}
+        subsets = self._subsets if self.config.subsets is None else self.config.subsets
+        for split, ss_names in subsets.items():
+            filtered_subsets[split] = []
+            for ss_name in ss_names:
+                dataset = DATASET_MAP[ss_name]
+                if dataset.target != target or source not in dataset.sources:
+                    logger.info("Skipping sub-dataset that does not include language pair: %s", ss_name)
+                else:
+                    filtered_subsets[split].append(ss_name)
+        logger.info("Using sub-datasets: %s", filtered_subsets)
+        return filtered_subsets
+
+    def _info(self):
+        src, target = self.config.language_pair
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"translation": datasets.features.Translation(languages=self.config.language_pair)}
+            ),
+            supervised_keys=(src, target),
+            homepage=self.config.url,
+            citation=self.config.citation,
+        )
+
+    def _vocab_text_gen(self, split_subsets, extraction_map, language):
+        for _, ex in self._generate_examples(split_subsets, extraction_map, with_translation=False):
+            yield ex[language]
+
+    def _split_generators(self, dl_manager):
+        source, _ = self.config.language_pair
+        manual_paths_dict = {}
+        urls_to_download = {}
+        for ss_name in itertools.chain.from_iterable(self.subsets.values()):
+
+            # get dataset
+            dataset = DATASET_MAP[ss_name]
+            if dataset.get_manual_dl_files(source):
+                # TODO(PVP): following two lines skip configs that are incomplete for now
+                # +++++++++++++++++++++
+                logger.info("Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
+                continue
+                # +++++++++++++++++++++
+
+                manual_dl_files = dataset.get_manual_dl_files(source)
+                manual_paths = [
+                    os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), fname)
+                    for fname in manual_dl_files
+                ]
+                assert all(
+                    os.path.exists(path) for path in manual_paths
+                ), f"For {dataset.name}, you must manually download the following file(s) from {dataset.get_url(source)} and place them in {dl_manager.manual_dir}: {', '.join(manual_dl_files)}"
+
+                # set manual path for correct subset
+                manual_paths_dict[ss_name] = manual_paths
+            else:
+                urls_to_download[ss_name] = dataset.get_url(source)
+
+        # Download and extract files from URLs.
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        # Extract manually downloaded files.
+        manual_files = dl_manager.extract(manual_paths_dict)
+        extraction_map = dict(downloaded_files, **manual_files)
+
+        for language in self.config.language_pair:
+            self._vocab_text_gen(self.subsets[datasets.Split.TRAIN], extraction_map, language)
+
+        return [
+            datasets.SplitGenerator(  # pylint:disable=g-complex-comprehension
+                name=split, gen_kwargs={"split_subsets": split_subsets, "extraction_map": extraction_map}
+            )
+            for split, split_subsets in self.subsets.items()
+        ]
+
+    def _generate_examples(self, split_subsets, extraction_map, with_translation=True):
+        """Returns the examples in the raw (text) form."""
+        source, _ = self.config.language_pair
+
+        def _get_local_paths(dataset, extract_dirs):
+            rel_paths = dataset.get_path(source)
+            if len(extract_dirs) == 1:
+                extract_dirs = extract_dirs * len(rel_paths)
+            return [
+                os.path.join(ex_dir, rel_path) if rel_path else ex_dir
+                for ex_dir, rel_path in zip(extract_dirs, rel_paths)
+            ]
+
+        def _get_filenames(dataset):
+            rel_paths = dataset.get_path(source)
+            urls = dataset.get_url(source)
+            if len(urls) == 1:
+                urls = urls * len(rel_paths)
+            return [rel_path if rel_path else os.path.basename(url) for url, rel_path in zip(urls, rel_paths)]
+
+        for ss_name in split_subsets:
+            # TODO(PVP) remove following five lines when manual data works
+            # +++++++++++++++++++++
+            dataset = DATASET_MAP[ss_name]
+            source, _ = self.config.language_pair
+            if dataset.get_manual_dl_files(source):
+                logger.info(f"Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
+                continue
+            # +++++++++++++++++++++
+
+            logger.info("Generating examples from: %s", ss_name)
+            dataset = DATASET_MAP[ss_name]
+            extract_dirs = extraction_map[ss_name]
+            files = _get_local_paths(dataset, extract_dirs)
+            filenames = _get_filenames(dataset)
+
+            sub_generator_args = tuple(files)
+
+            if ss_name.startswith("czeng"):
+                if ss_name.endswith("16pre"):
+                    sub_generator = functools.partial(_parse_tsv, language_pair=("en", "cs"))
+                    sub_generator_args += tuple(filenames)
+                else:
+                    sub_generator = _parse_czeng
+            elif ss_name == "hindencorp_01":
+                sub_generator = _parse_hindencorp
+            elif len(files) == 2:
+                if ss_name.endswith("_frde"):
+                    sub_generator = _parse_frde_bitext
+                else:
+                    sub_generator = _parse_parallel_sentences
+                    sub_generator_args += tuple(filenames)
+            elif len(files) == 1:
+                fname = filenames[0]
+                # Note: Due to formatting used by `download_manager`, the file
+                # extension may not be at the end of the file path.
+                if ".tsv" in fname:
+                    sub_generator = _parse_tsv
+                    sub_generator_args += tuple(filenames)
+                elif (
+                    ss_name.startswith("newscommentary_v14")
+                    or ss_name.startswith("europarl_v9")
+                    or ss_name.startswith("wikititles_v1")
+                ):
+                    sub_generator = functools.partial(_parse_tsv, language_pair=self.config.language_pair)
+                    sub_generator_args += tuple(filenames)
+                elif "tmx" in fname or ss_name.startswith("paracrawl_v3"):
+                    sub_generator = _parse_tmx
+                elif ss_name.startswith("wikiheadlines"):
+                    sub_generator = _parse_wikiheadlines
+                else:
+                    raise ValueError("Unsupported file format: %s" % fname)
+            else:
+                raise ValueError("Invalid number of files: %d" % len(files))
+
+            for sub_key, ex in sub_generator(*sub_generator_args):
+                if not all(ex.values()):
+                    continue
+                # TODO(adarob): Add subset feature.
+                # ex["subset"] = subset
+                key = f"{ss_name}/{sub_key}"
+                if with_translation is True:
+                    ex = {"translation": ex}
+                yield key, ex
+
+
+def _parse_parallel_sentences(f1, f2, filename1, filename2):
+    """Returns examples from parallel SGML or text files, which may be gzipped."""
+
+    def _parse_text(path, original_filename):
+        """Returns the sentences from a single text file, which may be gzipped."""
+        split_path = original_filename.split(".")
+
+        if split_path[-1] == "gz":
+            lang = split_path[-2]
+
+            def gen():
+                with open(path, "rb") as f, gzip.GzipFile(fileobj=f) as g:
+                    for line in g:
+                        yield line.decode("utf-8").rstrip()
+
+            return gen(), lang
+
+        if split_path[-1] == "txt":
+            # CWMT
+            lang = split_path[-2].split("_")[-1]
+            lang = "zh" if lang in ("ch", "cn", "c[hn]") else lang
+        else:
+            lang = split_path[-1]
+
+        def gen():
+            with open(path, "rb") as f:
+                for line in f:
+                    yield line.decode("utf-8").rstrip()
+
+        return gen(), lang
+
+    def _parse_sgm(path, original_filename):
+        """Returns sentences from a single SGML file."""
+        lang = original_filename.split(".")[-2]
+        # Note: We can't use the XML parser since some of the files are badly
+        # formatted.
+        seg_re = re.compile(r"<seg id=\"\d+\">(.*)</seg>")
+
+        def gen():
+            with open(path, encoding="utf-8") as f:
+                for line in f:
+                    seg_match = re.match(seg_re, line)
+                    if seg_match:
+                        assert len(seg_match.groups()) == 1
+                        yield seg_match.groups()[0]
+
+        return gen(), lang
+
+    parse_file = _parse_sgm if os.path.basename(f1).endswith(".sgm") else _parse_text
+
+    # Some datasets (e.g., CWMT) contain multiple parallel files specified with
+    # a wildcard. We sort both sets to align them and parse them one by one.
+    f1_files = sorted(glob.glob(f1))
+    f2_files = sorted(glob.glob(f2))
+
+    assert f1_files and f2_files, "No matching files found: %s, %s." % (f1, f2)
+    assert len(f1_files) == len(f2_files), "Number of files do not match: %d vs %d for %s vs %s." % (
+        len(f1_files),
+        len(f2_files),
+        f1,
+        f2,
+    )
+
+    for f_id, (f1_i, f2_i) in enumerate(zip(sorted(f1_files), sorted(f2_files))):
+        l1_sentences, l1 = parse_file(f1_i, filename1)
+        l2_sentences, l2 = parse_file(f2_i, filename2)
+
+        for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)):
+            key = f"{f_id}/{line_id}"
+            yield key, {l1: s1, l2: s2}
+
+
+def _parse_frde_bitext(fr_path, de_path):
+    with open(fr_path, encoding="utf-8") as fr_f:
+        with open(de_path, encoding="utf-8") as de_f:
+            for line_id, (s1, s2) in enumerate(zip(fr_f, de_f)):
+                yield line_id, {"fr": s1.rstrip(), "de": s2.rstrip()}
+
+
+def _parse_tmx(path):
+    """Generates examples from TMX file."""
+
+    def _get_tuv_lang(tuv):
+        for k, v in tuv.items():
+            if k.endswith("}lang"):
+                return v
+        raise AssertionError("Language not found in `tuv` attributes.")
+
+    def _get_tuv_seg(tuv):
+        segs = tuv.findall("seg")
+        assert len(segs) == 1, "Invalid number of segments: %d" % len(segs)
+        return segs[0].text
+
+    with open(path, "rb") as f:
+        # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
+        utf_f = codecs.getreader("utf-8")(f)
+        for line_id, (_, elem) in enumerate(ElementTree.iterparse(utf_f)):
+            if elem.tag == "tu":
+                yield line_id, {_get_tuv_lang(tuv): _get_tuv_seg(tuv) for tuv in elem.iterfind("tuv")}
+                elem.clear()
+
+
+def _parse_tsv(path, filename, language_pair=None):
+    """Generates examples from TSV file."""
+    if language_pair is None:
+        lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", filename)
+        assert lang_match is not None, "Invalid TSV filename: %s" % filename
+        l1, l2 = lang_match.groups()
+    else:
+        l1, l2 = language_pair
+    with open(path, encoding="utf-8") as f:
+        for j, line in enumerate(f):
+            cols = line.split("\t")
+            if len(cols) != 2:
+                logger.warning("Skipping line %d in TSV (%s) with %d != 2 columns.", j, path, len(cols))
+                continue
+            s1, s2 = cols
+            yield j, {l1: s1.strip(), l2: s2.strip()}
+
+
+def _parse_wikiheadlines(path):
+    """Generates examples from Wikiheadlines dataset file."""
+    lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path)
+    assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path
+    l1, l2 = lang_match.groups()
+    with open(path, encoding="utf-8") as f:
+        for line_id, line in enumerate(f):
+            s1, s2 = line.split("|||")
+            yield line_id, {l1: s1.strip(), l2: s2.strip()}
+
+
+def _parse_czeng(*paths, **kwargs):
+    """Generates examples from CzEng v1.6, with optional filtering for v1.7."""
+    filter_path = kwargs.get("filter_path", None)
+    if filter_path:
+        re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]")
+        with open(filter_path, encoding="utf-8") as f:
+            bad_blocks = {blk for blk in re.search(r"qw{([\s\d]*)}", f.read()).groups()[0].split()}
+        logger.info("Loaded %d bad blocks to filter from CzEng v1.6 to make v1.7.", len(bad_blocks))
+
+    for path in paths:
+        for gz_path in sorted(glob.glob(path)):
+            with open(gz_path, "rb") as g, gzip.GzipFile(fileobj=g) as f:
+                filename = os.path.basename(gz_path)
+                for line_id, line in enumerate(f):
+                    line = line.decode("utf-8")  # required for py3
+                    if not line.strip():
+                        continue
+                    id_, unused_score, cs, en = line.split("\t")
+                    if filter_path:
+                        block_match = re.match(re_block, id_)
+                        if block_match and block_match.groups()[0] in bad_blocks:
+                            continue
+                    sub_key = f"{filename}/{line_id}"
+                    yield sub_key, {
+                        "cs": cs.strip(),
+                        "en": en.strip(),
+                    }
+
+
+def _parse_hindencorp(path):
+    with open(path, encoding="utf-8") as f:
+        for line_id, line in enumerate(f):
+            split_line = line.split("\t")
+            if len(split_line) != 5:
+                logger.warning("Skipping invalid HindEnCorp line: %s", line)
+                continue
+            yield line_id, {"translation": {"en": split_line[3].strip(), "hi": split_line[4].strip()}}