Datasets:
Tasks:
Question Answering
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
multihop-tabular-text-qa
License:
File size: 6,525 Bytes
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# coding=utf-8
# Copyright 2020 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.
"""HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data"""
import json
import os
import datasets
_CITATION = """\
@article{chen2020hybridqa,
title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
journal={Findings of EMNLP 2020},
year={2020}
}
"""
_DESCRIPTION = """\
Existing question answering datasets focus on dealing with homogeneous information, based either only on text or \
KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, \
using homogeneous information alone might lead to severe coverage problems. \
To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that \
requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table \
and multiple free-form corpora linked with the entities in the table. The questions are designed \
to aggregate both tabular information and text information, i.e., \
lack of either form would render the question unanswerable.
"""
_HOMEPAGE = "https://hybridqa.github.io/index.html"
_WIKI_TABLES_GIT_ARCHIVE_URL = (
"https://github.com/wenhuchen/WikiTables-WithLinks/archive/f4ed68e54e25c495f63d309de0b89c0f97b3c508.zip"
)
_QA_DATA_BASE_URL = "https://raw.githubusercontent.com/wenhuchen/HybridQA/master/released_data"
_URLS = {
"train": f"{_QA_DATA_BASE_URL}/train.json",
"dev": f"{_QA_DATA_BASE_URL}/dev.json",
"test": f"{_QA_DATA_BASE_URL}/test.json",
}
class HybridQa(datasets.GeneratorBasedBuilder):
"""HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="hybrid_qa",
version=datasets.Version("1.0.0"),
),
]
def _info(self):
features = {
"question_id": datasets.Value("string"),
"question": datasets.Value("string"),
"table_id": datasets.Value("string"),
"answer_text": datasets.Value("string"),
"question_postag": datasets.Value("string"),
"table": {
"url": datasets.Value("string"),
"title": datasets.Value("string"),
"header": datasets.Sequence(datasets.Value("string")),
"data": [
{
"value": datasets.Value("string"),
"urls": [{"url": datasets.Value("string"), "summary": datasets.Value("string")}],
}
],
"section_title": datasets.Value("string"),
"section_text": datasets.Value("string"),
"uid": datasets.Value("string"),
"intro": datasets.Value("string"),
},
}
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
extracted_path = dl_manager.download_and_extract(_WIKI_TABLES_GIT_ARCHIVE_URL)
downloaded_files = dl_manager.download(_URLS)
repo_path = os.path.join(extracted_path, "WikiTables-WithLinks-f4ed68e54e25c495f63d309de0b89c0f97b3c508")
tables_path = os.path.join(repo_path, "tables_tok")
requests_path = os.path.join(repo_path, "request_tok")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"qa_filepath": downloaded_files["train"],
"tables_path": tables_path,
"requests_path": requests_path,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"qa_filepath": downloaded_files["dev"],
"tables_path": tables_path,
"requests_path": requests_path,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"qa_filepath": downloaded_files["test"],
"tables_path": tables_path,
"requests_path": requests_path,
},
),
]
def _generate_examples(self, qa_filepath, tables_path, requests_path):
with open(qa_filepath, encoding="utf-8") as f:
examples = json.load(f)
for example in examples:
table_id = example["table_id"]
table_file_path = os.path.join(tables_path, f"{table_id}.json")
url_data_path = os.path.join(requests_path, f"{table_id}.json")
with open(table_file_path, encoding="utf-8") as f:
table = json.load(f)
with open(url_data_path, encoding="utf-8") as f:
url_data = json.load(f)
table["header"] = [header[0] for header in table["header"]]
# here each row is a list with two elemets, the row value and list of urls for that row
# convert it to list of dict with keys value and urls
rows = []
for row in table["data"]:
for col in row:
new_row = {"value": col[0]}
urls = col[1]
new_row["urls"] = [{"url": url, "summary": url_data[url]} for url in urls]
rows.append(new_row)
table["data"] = rows
example["answer_text"] = example.pop("answer-text") if "answer-text" in example else ""
example["table"] = table
yield example["question_id"], example
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