File size: 6,525 Bytes
e3d13e5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
# 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://github.com/wenhuchen/HybridQA"

_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