msr_sqa / msr_sqa.py
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Fix bugs in msr_sqa dataset (#3715)
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors, The Google AI Language Team Authors and the current dataset script contributor.
#
# 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.
"""Microsoft Research Sequential Question Answering (SQA) Dataset"""
import ast
import csv
import os
import pandas as pd
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{iyyer2017search,
title={Search-based neural structured learning for sequential question answering},
author={Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei},
booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={1821--1831},
year={2017}
}
"""
_DESCRIPTION = """\
Recent work in semantic parsing for question answering has focused on long and complicated questions, \
many of which would seem unnatural if asked in a normal conversation between two humans. \
In an effort to explore a conversational QA setting, we present a more realistic task: \
answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers \
to decompose 2,022 questions from WikiTableQuestions (WTQ), which contains highly-compositional questions about \
tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences \
that contain 17,553 questions in total. Each question is also associated with answers in the form of cell \
locations in the tables.
"""
_HOMEPAGE = "https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2"
_LICENSE = "Microsoft Research Data License Agreement"
_URL = "https://download.microsoft.com/download/1/D/C/1DC270D2-1B53-4A61-A2E3-88AB3E4E6E1F/SQA%20Release%201.0.zip"
def _load_table_data(table_file):
"""Load additional data from a csv table file.
Args:
table_file: Path to the csv file.
Returns:
header: a list of headers in the table.
rows: 2d array of data in the table.
"""
rows = []
table_data = pd.read_csv(table_file)
# the first line is header
header = list(table_data.columns)
for row_data in table_data.values:
rows.append([str(_) for _ in list(row_data)])
return header, rows
def _parse_answer_coordinates(answer_coordinate_str):
"""Parsing answer_coordinates field to a list of answer coordinates.
The original code is from https://github.com/google-research/tapas.
Args:
answer_coordinate_str: A string representation of a Python list of tuple
strings.
For example: "['(1, 4)','(1, 3)', ...]"
Returns:
answer_coordinates: A list of answer cordinates.
"""
try:
answer_coordinates = []
coords = ast.literal_eval(answer_coordinate_str)
for row_index, column_index in sorted(ast.literal_eval(coord) for coord in coords):
answer_coordinates.append({"row_index": row_index, "column_index": column_index})
return answer_coordinates
except SyntaxError:
raise ValueError("Unable to evaluate %s" % answer_coordinate_str)
def _parse_answer_text(answer_text_str):
"""Parsing `answer_text` field to list of answers.
The original code is from https://github.com/google-research/tapas.
Args:
answer_text_str: A string representation of a Python list of strings.
For example: "[u'test', u'hello', ...]"
Returns:
answer_texts: A list of answers.
"""
try:
answer_texts = []
for value in ast.literal_eval(answer_text_str):
answer_texts.append(value)
return answer_texts
except SyntaxError:
raise ValueError("Unable to evaluate %s" % answer_text_str)
class MsrSQA(datasets.GeneratorBasedBuilder):
"""Microsoft Research Sequential Question Answering (SQA) Dataset"""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"annotator": datasets.Value("int32"),
"position": datasets.Value("int32"),
"question": datasets.Value("string"),
"question_and_history": datasets.Sequence(datasets.Value("string")),
"table_file": datasets.Value("string"),
"table_header": datasets.features.Sequence(datasets.Value("string")),
"table_data": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
"answer_coordinates": datasets.features.Sequence(
{"row_index": datasets.Value("int32"), "column_index": datasets.Value("int32")}
),
"answer_text": datasets.features.Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = os.path.join(dl_manager.download_and_extract(_URL), "SQA Release 1.0")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(data_dir, "random-split-1-train.tsv"), "data_dir": data_dir},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(data_dir, "random-split-1-dev.tsv"), "data_dir": data_dir},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv"), "data_dir": data_dir},
),
]
def _generate_examples(self, filepath, data_dir):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
question_and_history = []
for idx, item in enumerate(reader):
item["answer_text"] = _parse_answer_text(item["answer_text"])
item["answer_coordinates"] = _parse_answer_coordinates(item["answer_coordinates"])
header, table_data = _load_table_data(os.path.join(data_dir, item["table_file"]))
item["table_header"] = header
item["table_data"] = table_data
if item["position"] == "0":
question_and_history = [] # reset history
question_and_history.append(item["question"])
item["question_and_history"] = question_and_history
yield idx, item