Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
conversational-qa
License:
# 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 | |