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tydiqa.py

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+import json
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = r"""\
+@article{clark-etal-2020-tydi,
+    title = "{T}y{D}i {QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages",
+    author = "Clark, Jonathan H.  and
+      Choi, Eunsol  and
+      Collins, Michael  and
+      Garrette, Dan  and
+      Kwiatkowski, Tom  and
+      Nikolaev, Vitaly  and
+      Palomaki, Jennimaria",
+    editor = "Johnson, Mark  and
+      Roark, Brian  and
+      Nenkova, Ani",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "8",
+    year = "2020",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/2020.tacl-1.30",
+    doi = "10.1162/tacl_a_00317",
+    pages = "454--470",
+    abstract = "Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations.
+    We present TyDi QA{---}a question answering dataset covering 11 typologically diverse languages with 204K
+    question-answer pairs. The languages of TyDi QA are diverse with regard to their typology{---}the set of
+    linguistic features each language expresses{---}such that we expect models performing well on this set to
+    generalize across a large number of the world{'}s languages. We present a quantitative analysis of the data
+    quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found
+    in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are
+    written by people who want to know the answer, but don{'}t know the answer yet, and the data is collected directly
+    in each language without the use of translation.",
+}
+
+@inproceedings{cahyawijaya-etal-2021-indonlg,
+    title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation",
+    author = "Cahyawijaya, Samuel  and
+      Winata, Genta Indra  and
+      Wilie, Bryan  and
+      Vincentio, Karissa  and
+      Li, Xiaohong  and
+      Kuncoro, Adhiguna  and
+      Ruder, Sebastian  and
+      Lim, Zhi Yuan  and
+      Bahar, Syafri  and
+      Khodra, Masayu  and
+      Purwarianti, Ayu  and
+      Fung, Pascale",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.699",
+    doi = "10.18653/v1/2021.emnlp-main.699",
+    pages = "8875--8898"
+}
+"""
+
+_DATASETNAME = "tydiqa"
+
+_DESCRIPTION = """\
+    TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
+    The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
+    expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
+    in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
+    information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
+    don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language
+    without the use of translation (unlike MLQA and XQuAD).
+    """
+
+_HOMEPAGE = "https://github.com/google-research-datasets/tydiqa"
+_LICENSE = Licenses.APACHE_2_0.value
+_HF_URL = "https://huggingface.co/datasets/tydiqa"
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+_LANGUAGES = ["ind", "tha"]
+_LOCAL = False
+_SOURCE_VERSION = "1.0.0"
+_SOURCE_VERSION_P = "1.0.0"
+_SOURCE_VERSION_S = "1.1.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_URL = "https://storage.googleapis.com/tydiqa/"
+_PRIMARY_URLS = {
+    "train": _URL + "v1.0/tydiqa-v1.0-train.jsonl.gz",
+    "dev": _URL + "v1.0/tydiqa-v1.0-dev.jsonl.gz",
+}
+_SECONDARY_URLS = {
+    "train": _URL + "v1.1/tydiqa-goldp-v1.1-train.json",
+    "dev": _URL + "v1.1/tydiqa-goldp-v1.1-dev.json",
+}
+
+_SELECTP_DESP = """Passage selection task (SelectP): Given a list of the passages in the article, return either (a) the index of
+          the passage that answers the question or (b) NULL if no such passage exists.
+          """
+_MINSPAN_DESP = """Minimal answer span task (MinSpan): Given the full text of an article, return one of (a) the start and end
+          byte indices of the minimal span that completely answers the question; (b) YES or NO if the question requires
+          a yes/no answer and we can draw a conclusion from the passage; (c) NULL if it is not possible to produce a
+          minimal answer for this question."""
+_GOLDP_DESP = """Gold passage task (GoldP): Given a passage that is guaranteed to contain the
+          answer, predict the single contiguous span of characters that answers the question. This is more similar to
+          existing reading comprehension datasets (as opposed to the information-seeking task outlined above).
+          """
+_ID_DESP = """{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation, is a benchmark
+          for evaluating Indonesian natural language generation (NLG) systems. The question-answer pairs are collected
+          for each language without using translation services. It uses the Indonesian data from the secondary Gold
+          passage task of the TyDiQA dataset.  As the original dataset only provides training and validation sets,
+          TydiQA-ID randomly split off 15% of the training data and use it as the test set.
+          """
+
+
+def config_constructor(subset_id, schema, desc, version):
+    return SEACrowdConfig(name=f"{_DATASETNAME}_{subset_id}_{schema}", description=desc, version=datasets.Version(version), schema=schema, subset_id=subset_id)
+
+
+class TydiqaDataset(datasets.GeneratorBasedBuilder):
+    """
+    This is a main class of SEACrowd dataloader for TyDi QA, which is a question answering dataset covering 11 typologically
+    diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology.
+    Here we also specially provide the split on the primary and secondary task for SEA language like indonesian and thai.
+    """
+
+    BUILDER_CONFIGS = [
+        # source schema
+        # selectp source schema
+        config_constructor(subset_id="selectp", schema="source", desc=_SELECTP_DESP, version=_SOURCE_VERSION_P),
+        config_constructor(subset_id="selectp_ind", schema="source", desc=_SELECTP_DESP, version=_SOURCE_VERSION_P),
+        config_constructor(subset_id="selectp_tha", schema="source", desc=_SELECTP_DESP, version=_SOURCE_VERSION_P),
+        # minspan source schema
+        config_constructor(subset_id="minspan", schema="source", desc=_MINSPAN_DESP, version=_SOURCE_VERSION_P),
+        config_constructor(subset_id="minspan_ind", schema="source", desc=_MINSPAN_DESP, version=_SOURCE_VERSION_P),
+        config_constructor(subset_id="minspan_tha", schema="source", desc=_MINSPAN_DESP, version=_SOURCE_VERSION_P),
+        # goldp source schema
+        config_constructor(subset_id="goldp", schema="source", desc=_GOLDP_DESP, version=_SOURCE_VERSION_S),
+        config_constructor(subset_id="goldp_ind", schema="source", desc=_GOLDP_DESP, version=_SOURCE_VERSION_S),
+        # tydiqa_id source schema
+        config_constructor(subset_id="id", schema="source", desc=_ID_DESP, version=_SOURCE_VERSION_P),
+        # seacrowd schema
+        # selectp seacrowd schema
+        config_constructor(subset_id="selectp", schema="seacrowd_qa", desc=_SELECTP_DESP, version=_SEACROWD_VERSION),
+        config_constructor(subset_id="selectp_ind", schema="seacrowd_qa", desc=_SELECTP_DESP, version=_SEACROWD_VERSION),
+        config_constructor(subset_id="selectp_tha", schema="seacrowd_qa", desc=_SELECTP_DESP, version=_SEACROWD_VERSION),
+        # minspan seacrowd schema
+        config_constructor(subset_id="minspan", schema="seacrowd_qa", desc=_MINSPAN_DESP, version=_SEACROWD_VERSION),
+        config_constructor(subset_id="minspan_ind", schema="seacrowd_qa", desc=_MINSPAN_DESP, version=_SEACROWD_VERSION),
+        config_constructor(subset_id="minspan_tha", schema="seacrowd_qa", desc=_MINSPAN_DESP, version=_SEACROWD_VERSION),
+        # goldp seacrowd schema
+        config_constructor(subset_id="goldp", schema="seacrowd_qa", desc=_GOLDP_DESP, version=_SEACROWD_VERSION),
+        config_constructor(subset_id="goldp_ind", schema="seacrowd_qa", desc=_GOLDP_DESP, version=_SEACROWD_VERSION),
+        # tydiqa_id seacrowd schema
+        config_constructor(subset_id="id", schema="seacrowd_qa", desc=_ID_DESP, version=_SEACROWD_VERSION),
+    ]
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_id_source"
+
+    def _info(self):
+        if ("selectp" in self.config.name) or ("minspan" in self.config.name):
+            if "source" in self.config.name:
+                features = datasets.Features(
+                    {
+                        "passage_answer_candidates": datasets.features.Sequence(
+                            {
+                                "plaintext_start_byte": datasets.Value("int32"),
+                                "plaintext_end_byte": datasets.Value("int32"),
+                            }
+                        ),
+                        "question_text": datasets.Value("string"),
+                        "document_title": datasets.Value("string"),
+                        "language": datasets.Value("string"),
+                        "annotations": datasets.features.Sequence(
+                            {
+                                "passage_answer_candidate_index": datasets.Value("int32"),
+                                "minimal_answers_start_byte": datasets.Value("int32"),
+                                "minimal_answers_end_byte": datasets.Value("int32"),
+                                "yes_no_answer": datasets.Value("string"),
+                            }
+                        ),
+                        "document_plaintext": datasets.Value("string"),
+                        "document_url": datasets.Value("string"),
+                    }
+                )
+            elif "seacrowd" in self.config.name:
+                features = schemas.qa_features
+                features["meta"] = {
+                    "passage_answer_candidates": datasets.features.Sequence(
+                        {
+                            "plaintext_start_byte": datasets.Value("int32"),
+                            "plaintext_end_byte": datasets.Value("int32"),
+                        }
+                    ),
+                    "annotations": datasets.features.Sequence(
+                        {
+                            "passage_answer_candidate_index": datasets.Value("int32"),
+                            "minimal_answers_start_byte": datasets.Value("int32"),
+                            "minimal_answers_end_byte": datasets.Value("int32"),
+                            "yes_no_answer": datasets.Value("string"),
+                        }
+                    ),
+                    "language": datasets.Value("string"),
+                }
+
+        elif ("goldp" in self.config.name) or ("tydiqa_id" in self.config.name):
+            if "source" in self.config.name:
+                features = datasets.Features(
+                    {
+                        "id": datasets.Value("string"),
+                        "title": datasets.Value("string"),
+                        "context": datasets.Value("string"),
+                        "question": datasets.Value("string"),
+                        "answers": datasets.features.Sequence(
+                            {
+                                "text": datasets.Value("string"),
+                                "answer_start": datasets.Value("int32"),
+                            }
+                        ),
+                    }
+                )
+            elif "seacrowd" in self.config.name:
+                features = schemas.qa_features
+                features["meta"] = {
+                    "answer_start": datasets.Sequence(datasets.Value("int32")),
+                }
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            citation=_CITATION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        primary_downloaded = dl_manager.download_and_extract(_PRIMARY_URLS)
+        secondary_downloaded = dl_manager.download_and_extract(_SECONDARY_URLS)
+
+        if ("selectp" in self.config.name) or ("minspan" in self.config.name):
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": primary_downloaded["train"]},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": primary_downloaded["dev"]},
+                ),
+            ]
+
+        elif "goldp" in self.config.name:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": secondary_downloaded["train"]},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": secondary_downloaded["dev"]},
+                ),
+            ]
+        elif "tydiqa_id" in self.config.name:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": secondary_downloaded["train"], "split": "train"},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": secondary_downloaded["train"], "split": "test"},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": secondary_downloaded["dev"], "split": "validation"},
+                ),
+            ]
+
+    def _generate_examples(self, filepath, split=None):
+        """Yields examples."""
+
+        if ("selectp" in self.config.name) or ("minspan" in self.config.name):
+            with open(filepath, encoding="utf-8") as f:
+                for id_, row in enumerate(f):
+                    data = json.loads(row)
+                    passages = data["passage_answer_candidates"]
+                    end_byte = [passage["plaintext_end_byte"] for passage in passages]
+                    start_byte = [passage["plaintext_start_byte"] for passage in passages]
+                    title = data["document_title"]
+                    lang = data["language"]
+                    question = data["question_text"]
+                    annotations = data["annotations"]
+                    yes_no_answers = [annotation["yes_no_answer"] for annotation in annotations]
+                    min_answers_end_byte = [annotation["minimal_answer"]["plaintext_end_byte"] for annotation in annotations]
+                    min_answers_start_byte = [annotation["minimal_answer"]["plaintext_start_byte"] for annotation in annotations]
+                    passage_cand_answers = [annotation["passage_answer"]["candidate_index"] for annotation in annotations]
+                    doc = data["document_plaintext"]
+                    url = data["document_url"]
+                    if (self.config.name == "tydiqa_selectp_source") or (self.config.name == "tydiqa_minspan_source"):
+                        yield id_, primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url)
+                    elif (self.config.name == "tydiqa_selectp_ind_source") or (self.config.name == "tydiqa_minspan_ind_source"):
+                        if lang == "indonesian":
+                            yield id_, primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url)
+                    elif (self.config.name == "tydiqa_selectp_tha_source") or (self.config.name == "tydiqa_minspan_tha_source"):
+                        if lang == "thai":
+                            yield id_, primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url)
+                    # seacrowd
+                    elif (self.config.name == "tydiqa_selectp_seacrowd_qa") or (self.config.name == "tydiqa_minspan_seacrowd_qa"):
+                        yield id_, primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang)
+                    elif (self.config.name == "tydiqa_selectp_ind_seacrowd_qa") or (self.config.name == "tydiqa_minspan_ind_seacrowd_qa"):
+                        if lang == "indonesian":
+                            yield id_, primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang)
+                    elif (self.config.name == "tydiqa_selectp_tha_seacrowd_qa") or (self.config.name == "tydiqa_minspan_tha_seacrowd_qa"):
+                        if lang == "thai":
+                            yield id_, primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang)
+                    else:
+                        raise ValueError(f"No configs to match {self.config.name} in primary_task")
+
+        elif ("goldp" in self.config.name) or ("tydiqa_id" in self.config.name):
+            with (open(filepath, encoding="utf-8") as f):
+                data = json.load(f)
+                tydiqa_id_num = 0
+                for article in data["data"]:
+                    title = article.get("title", "").strip()
+                    for paragraph in article["paragraphs"]:
+                        context = paragraph["context"].strip()
+                        for qa in paragraph["qas"]:
+                            question = qa["question"].strip()
+                            id_ = qa["id"]
+                            answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                            answers = [answer["text"].strip() for answer in qa["answers"]]
+                            if self.config.name == "tydiqa_goldp_source":
+                                yield id_, second_source_helper(id_, title, context, question, answer_starts, answers)
+
+                            elif self.config.name == "tydiqa_goldp_ind_source":
+                                if id_.startswith("indonesian"):
+                                    yield id_, second_source_helper(id_, title, context, question, answer_starts, answers)
+                            elif self.config.name == "tydiqa_id_source":
+                                if id_.startswith("indonesian"):
+                                    tydiqa_id_num += 1
+                                    if split == "train" and tydiqa_id_num >= 856:
+                                        yield id_, second_source_helper(id_, title, context, question, answer_starts, answers)
+                                    if split == "test" and tydiqa_id_num < 856:
+                                        yield id_, second_source_helper(id_, title, context, question, answer_starts, answers)
+                                    if split == "validation":
+                                        yield id_, second_source_helper(id_, title, context, question, answer_starts, answers)
+
+                            elif self.config.name == "tydiqa_goldp_seacrowd_qa":
+                                yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts)
+                            elif self.config.name == "tydiqa_goldp_ind_seacrowd_qa":
+                                if id_.startswith("indonesian"):
+                                    yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts)
+                            elif self.config.name == "tydiqa_id_seacrowd_qa":
+                                if id_.startswith("indonesian"):
+                                    tydiqa_id_num += 1
+                                    if split == "train" and tydiqa_id_num >= 856:
+                                        yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts)
+                                    if split == "test" and tydiqa_id_num < 856:
+                                        yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts)
+                                    if split == "validation":
+                                        yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts)
+                            else:
+                                raise ValueError(f"No configs to match {self.config.name} in secondary_task")
+
+
+def primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url):
+    return {
+        "passage_answer_candidates": {
+            "plaintext_start_byte": start_byte,
+            "plaintext_end_byte": end_byte,
+        },
+        "question_text": question,
+        "document_title": title,
+        "language": lang,
+        "annotations": {
+            "passage_answer_candidate_index": passage_cand_answers,
+            "minimal_answers_start_byte": min_answers_start_byte,
+            "minimal_answers_end_byte": min_answers_end_byte,
+            "yes_no_answer": yes_no_answers,
+        },
+        "document_plaintext": doc,
+        "document_url": url,
+    }
+
+
+def primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang):
+    return {
+        "id": str(id_),
+        "question_id": title,
+        "document_id": title,
+        "question": question,
+        "type": "multiple_choice",
+        "choices": [""],
+        "context": doc,
+        "answer": [""],
+        "meta": {
+            "passage_answer_candidates": {
+                "plaintext_start_byte": start_byte,
+                "plaintext_end_byte": end_byte,
+            },
+            "annotations": {
+                "passage_answer_candidate_index": passage_cand_answers,
+                "minimal_answers_start_byte": min_answers_start_byte,
+                "minimal_answers_end_byte": min_answers_end_byte,
+                "yes_no_answer": yes_no_answers,
+            },
+            "language": lang,
+        },
+    }
+
+
+def second_source_helper(id_, title, context, question, answer_starts, answers):
+    return {
+        "title": title,
+        "context": context,
+        "question": question,
+        "id": id_,
+        "answers": {
+            "answer_start": answer_starts,
+            "text": answers,
+        },
+    }
+
+
+def second_seacrowd_helper(id_, question, context, answers, answer_starts):
+    return {
+        "id": id_,
+        "question_id": id_,
+        "document_id": id_,
+        "question": question,
+        "type": "abstractive",
+        "choices": [],
+        "context": context,
+        "answer": answers,
+        "meta": {"answer_start": answer_starts},
+    }