americasnlp-mt-21.py

# coding=utf-8
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# 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"],
        }