Datasets:
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"""XOR QA: Cross-lingual Open-Retrieval Question Answering"""
from __future__ import absolute_import, division, print_function
import json
import textwrap
import datasets
_XOR_TYDI_QA_CITATION = """\
@misc{asai2020xor,
title={XOR QA: Cross-lingual Open-Retrieval Question Answering},
author={Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},
year={2020},
eprint={2010.11856},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_XOR_TYDI_QA_DESCRIPTION = """\
XOR-TyDi QA brings together for the first time information-seeking questions,
open-retrieval QA, and multilingual QA to create a multilingual open-retrieval
QA dataset that enables cross-lingual answer retrieval. It consists of questions
written by information-seeking native speakers in 7 typologically diverse languages
and answer annotations that are retrieved from multilingual document collections.
There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.
"""
_DESCRIPTIONS = {
"xor-retrieve": textwrap.dedent(
"""\
XOR-Retrieve is a cross-lingual retrieval task where a question is written in the target
language (e.g., Japanese) and a system is required to retrieve English document that answers the question.
"""
),
"xor-full": textwrap.dedent(
"""\
XOR-Full is a cross-lingual retrieval task where a question is written in the target
language (e.g., Japanese) and a system is required to output a short answer in the target language."""
),
}
_DATA_URLS = {
"xor-retrieve": {
"train": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_retrieve_eng_span.jsonl",
"dev": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_retrieve_eng_span.jsonl",
"test": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_retrieve_eng_span_q_only.jsonl",
},
"xor-full": {
"train": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_full.jsonl",
"dev": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_full.jsonl",
"test": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_full_q_only.jsonl",
},
}
_XOR_TYDI_QA_URL = "https://nlp.cs.washington.edu/xorqa/"
class XORTyDiConfig(datasets.BuilderConfig):
"BuilderConfig for XOR-TyDi Dataset"
def __init__(self, data_url, citation, url, **kwargs):
"""
Args:
data_url: `dictionary`, dict with url for each split of data.
citation: `string`, citation for the dataset.
url: `string`, url for information about the dataset.
**kwargs: keyword arguments forwarded to super.
"""
super(XORTyDiConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.data_url = data_url
self.citation = citation
self.url = url
class XORTyDi(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
XORTyDiConfig(
name=name,
description=_DESCRIPTIONS[name],
data_url=_DATA_URLS[name],
citation=_XOR_TYDI_QA_CITATION,
url=_XOR_TYDI_QA_URL,
)
for name in ["xor-retrieve", "xor-full"]
]
def _info(self):
features = {}
features["question"] = datasets.Value("string")
features["lang"] = datasets.features.ClassLabel(names=["ar", "bn", "fi", "ja", "ko", "ru", "te"])
features["answers"] = datasets.Value("string")
return datasets.DatasetInfo(
description=_XOR_TYDI_QA_DESCRIPTION + "\n" + self.config.description,
features=datasets.Features(features),
homepage=self.config.url,
citation=_XOR_TYDI_QA_CITATION,
)
def _split_generators(self, dl_manager):
train = dl_manager.download_and_extract(self.config.data_url["train"])
dev = dl_manager.download_and_extract(self.config.data_url["dev"])
test = dl_manager.download_and_extract(self.config.data_url["test"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
jlines = f.read()
result = [json.loads(jline) for jline in jlines.splitlines()]
if split == "test":
for id_, row in enumerate(result):
yield id_, {"question": row["question"], "answers": "None", "lang": row["lang"].strip()}
else:
for id_, row in enumerate(result):
yield id_, {
"question": row["question"],
"answers": " ".join(row["answers"]),
"lang": row["lang"].strip(),
}
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