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extractive-qa
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License:
sharc / sharc.py
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets 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.
"""ShARC: A Conversational Question Answering dataset focussing on question answering from texts containing rules."""
import json
import os
import datasets
_CITATION = """\
@misc{saeidi2018interpretation,
title={Interpretation of Natural Language Rules in Conversational Machine Reading},
author={Marzieh Saeidi and Max Bartolo and Patrick Lewis and Sameer Singh and Tim Rocktäschel and Mike Sheldon and Guillaume Bouchard and Sebastian Riedel},
year={2018},
eprint={1809.01494},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
ShARC is a Conversational Question Answering dataset focussing on question answering from texts containing rules. \
The goal is to answer questions by possibly asking follow-up questions first. It is assumed assume that the question is often underspecified, \
in the sense that the question does not provide enough information to be answered directly. However, an agent can use the supporting rule text to \
infer what needs to be asked in order to determine the final answer.
"""
_URL = "https://sharc-data.github.io/data/sharc1-official.zip"
class Sharc(datasets.GeneratorBasedBuilder):
"""ShARC: A Conversational Question Answering dataset focussing on question answering from texts containing rules."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="sharc", version=datasets.Version("1.0.0")),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"utterance_id": datasets.Value("string"),
"source_url": datasets.Value("string"),
"snippet": datasets.Value("string"),
"question": datasets.Value("string"),
"scenario": datasets.Value("string"),
"history": [
{"follow_up_question": datasets.Value("string"), "follow_up_answer": datasets.Value("string")}
],
"evidence": [
{"follow_up_question": datasets.Value("string"), "follow_up_answer": datasets.Value("string")}
],
"answer": datasets.Value("string"),
"negative_question": datasets.Value("bool_"),
"negative_scenario": datasets.Value("bool_"),
}
),
supervised_keys=None,
homepage="https://sharc-data.github.io/index.html",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
extracted_path = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_dir": os.path.join(extracted_path, "sharc1-official"), "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_dir": os.path.join(extracted_path, "sharc1-official"), "split": "dev"},
),
]
def _generate_examples(self, data_dir, split):
with open(
os.path.join(data_dir, "negative_sample_utterance_ids", "sharc_negative_scenario_utterance_ids.txt"),
encoding="utf-8",
) as f:
negative_scenario_ids = f.readlines()
negative_scenario_ids = [id_.strip() for id_ in negative_scenario_ids]
with open(
os.path.join(data_dir, "negative_sample_utterance_ids", "sharc_negative_question_utterance_ids.txt"),
encoding="utf-8",
) as f:
negative_question_ids = f.readlines()
negative_question_ids = [id_.strip() for id_ in negative_question_ids]
data_file = os.path.join(data_dir, "json", f"sharc_{split}.json")
with open(data_file, encoding="utf-8") as f:
examples = json.load(f)
for i, example in enumerate(examples):
example.pop("tree_id")
example["negative_question"] = example["utterance_id"] in negative_question_ids
example["negative_scenario"] = example["utterance_id"] in negative_scenario_ids
example["id"] = example["utterance_id"]
# the keys are misspelled for one of the example in dev set
# fix it here
for evidence in example["evidence"]:
if evidence.get("followup_answer") is not None:
evidence["follow_up_answer"] = evidence.pop("followup_answer")
if evidence.get("followup_question") is not None:
evidence["follow_up_question"] = evidence.pop("followup_question")
yield example["id"], example