File size: 14,120 Bytes
88c0185 68c5599 88c0185 1743b7a 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 68c5599 88c0185 1743b7a 88c0185 68c5599 88c0185 1743b7a 88c0185 61a0823 68c5599 88c0185 1743b7a 88c0185 acede68 88c0185 61a0823 68c5599 acede68 88c0185 68c5599 88c0185 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 |
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and 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.
# Lint as: python3
"""Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v1.0.1"""
import json
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{feng-etal-2020-doc2dial,
title = "doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset",
author = "Feng, Song and Wan, Hui and Gunasekara, Chulaka and Patel, Siva and Joshi, Sachindra and Lastras, Luis",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.652",
}
"""
_DESCRIPTION = """\
Doc2dial is dataset of goal-oriented dialogues that are grounded in the associated documents. \
It includes over 4500 annotated conversations with an average of 14 turns that are grounded \
in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets \
this dataset covers a variety of dialogue scenes in information-seeking conversations.
"""
_HOMEPAGE = "https://doc2dial.github.io"
_URLs = "https://doc2dial.github.io/file/doc2dial_v1.0.1.zip"
class Doc2dial(datasets.GeneratorBasedBuilder):
"Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v1.0.1"
VERSION = datasets.Version("1.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="dialogue_domain",
version=VERSION,
description="This part of the dataset covers the dialgoue domain that has questions, answers and the associated doc ids",
),
datasets.BuilderConfig(
name="document_domain",
version=VERSION,
description="This part of the dataset covers the document domain which details all the documents in the various domains",
),
datasets.BuilderConfig(
name="doc2dial_rc",
version=VERSION,
description="Load Doc2Dial dataset for machine reading comprehension tasks",
),
]
DEFAULT_CONFIG_NAME = "dialogue_domain"
def _info(self):
if self.config.name == "dialogue_domain":
features = datasets.Features(
{
"dial_id": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"domain": datasets.Value("string"),
"turns": [
{
"turn_id": datasets.Value("int32"),
"role": datasets.Value("string"),
"da": datasets.Value("string"),
"references": [
{
"sp_id": datasets.Value("string"),
"label": datasets.Value("string"),
}
],
"utterance": datasets.Value("string"),
}
],
}
)
elif self.config.name == "document_domain":
features = datasets.Features(
{
"domain": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"title": datasets.Value("string"),
"doc_text": datasets.Value("string"),
"spans": [
{
"id_sp": datasets.Value("string"),
"tag": datasets.Value("string"),
"start_sp": datasets.Value("int32"),
"end_sp": datasets.Value("int32"),
"text_sp": datasets.Value("string"),
"title": datasets.Value("string"),
"parent_titles": datasets.Value("string"),
"id_sec": datasets.Value("string"),
"start_sec": datasets.Value("int32"),
"text_sec": datasets.Value("string"),
"end_sec": datasets.Value("int32"),
}
],
"doc_html_ts": datasets.Value("string"),
"doc_html_raw": datasets.Value("string"),
}
)
elif self.config.name == "doc2dial_rc":
features = datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
"domain": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
my_urls = _URLs
data_dir = dl_manager.download_and_extract(my_urls)
if self.config.name == "dialogue_domain":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_validation.json"),
},
),
]
elif self.config.name == "document_domain":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_doc.json"),
},
)
]
elif self.config.name == "doc2dial_rc":
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_validation.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_train.json"),
},
),
]
def _load_doc_data_rc(self, filepath):
doc_filepath = os.path.join(os.path.dirname(filepath), "doc2dial_doc.json")
with open(doc_filepath, encoding="utf-8") as f:
data = json.load(f)["doc_data"]
return data
def _get_answers_rc(self, references, spans, doc_text):
"""Obtain the grounding annotation for a given dialogue turn"""
if not references:
return []
start, end = -1, -1
ls_sp = []
for ele in references:
sp_id = ele["sp_id"]
start_sp, end_sp = spans[sp_id]["start_sp"], spans[sp_id]["end_sp"]
if start == -1 or start > start_sp:
start = start_sp
if end < end_sp:
end = end_sp
ls_sp.append(doc_text[start_sp:end_sp])
answer = {
"text": doc_text[start:end],
"answer_start": start,
}
return [answer]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
if self.config.name == "dialogue_domain":
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for domain in data["dial_data"]:
for doc_id in data["dial_data"][domain]:
for dialogue in data["dial_data"][domain][doc_id]:
x = {
"dial_id": dialogue["dial_id"],
"domain": domain,
"doc_id": doc_id,
"turns": dialogue["turns"],
}
yield dialogue["dial_id"], x
elif self.config.name == "document_domain":
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for domain in data["doc_data"]:
for doc_id in data["doc_data"][domain]:
yield doc_id, {
"domain": domain,
"doc_id": doc_id,
"title": data["doc_data"][domain][doc_id]["title"],
"doc_text": data["doc_data"][domain][doc_id]["doc_text"],
"spans": [
{
"id_sp": data["doc_data"][domain][doc_id]["spans"][i]["id_sp"],
"tag": data["doc_data"][domain][doc_id]["spans"][i]["tag"],
"start_sp": data["doc_data"][domain][doc_id]["spans"][i]["start_sp"],
"end_sp": data["doc_data"][domain][doc_id]["spans"][i]["end_sp"],
"text_sp": data["doc_data"][domain][doc_id]["spans"][i]["text_sp"],
"title": data["doc_data"][domain][doc_id]["spans"][i]["title"],
"parent_titles": str(
data["doc_data"][domain][doc_id]["spans"][i]["parent_titles"]
),
"id_sec": data["doc_data"][domain][doc_id]["spans"][i]["id_sec"],
"start_sec": data["doc_data"][domain][doc_id]["spans"][i]["start_sec"],
"text_sec": data["doc_data"][domain][doc_id]["spans"][i]["text_sec"],
"end_sec": data["doc_data"][domain][doc_id]["spans"][i]["end_sec"],
}
for i in data["doc_data"][domain][doc_id]["spans"]
],
"doc_html_ts": data["doc_data"][domain][doc_id]["doc_html_ts"],
"doc_html_raw": data["doc_data"][domain][doc_id]["doc_html_raw"],
}
elif self.config.name == "doc2dial_rc":
"""Load dialog data in the reading comprehension task setup, where context is the grounding document,
input query is dialog history in reversed order, and output to predict is the next agent turn."""
logger.info("generating examples from = %s", filepath)
doc_data = self._load_doc_data_rc(filepath)
with open(filepath, encoding="utf-8") as f:
dial_data = json.load(f)["dial_data"]
for domain, d_doc_dials in dial_data.items():
for doc_id, dials in d_doc_dials.items():
doc = doc_data[domain][doc_id]
for dial in dials:
all_prev_utterances = []
for idx, turn in enumerate(dial["turns"]):
all_prev_utterances.append(f"\t{turn['role']}:{turn['utterance']}")
if turn["role"] == "agent":
continue
if idx + 1 < len(dial["turns"]):
if dial["turns"][idx + 1]["role"] == "agent":
turn_to_predict = dial["turns"][idx + 1]
else:
continue
else:
continue
question = " ".join(list(reversed(all_prev_utterances))).strip()
id_ = f"{dial['dial_id']}_{turn['turn_id']}"
qa = {
"id": id_,
"title": doc_id,
"context": doc["doc_text"],
"question": question,
"answers": self._get_answers_rc(
turn_to_predict["references"],
doc["spans"],
doc["doc_text"],
),
"domain": domain,
}
yield id_, qa
|