zqwerty
commited on
Commit
•
39d73d2
1
Parent(s):
8838d5e
add CrossWOZ script
Browse files- CrossWOZ.py +298 -0
CrossWOZ.py
ADDED
@@ -0,0 +1,298 @@
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1 |
+
# coding=utf-8
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2 |
+
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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3 |
+
#
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4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
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7 |
+
#
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8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
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9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
"""CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset"""
|
16 |
+
|
17 |
+
|
18 |
+
import json
|
19 |
+
import os
|
20 |
+
|
21 |
+
import datasets
|
22 |
+
|
23 |
+
|
24 |
+
_CITATION = """\
|
25 |
+
@article{zhu2020crosswoz,
|
26 |
+
author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang},
|
27 |
+
title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset},
|
28 |
+
journal = {Transactions of the Association for Computational Linguistics},
|
29 |
+
year = {2020}
|
30 |
+
}
|
31 |
+
"""
|
32 |
+
|
33 |
+
_DESCRIPTION = """\
|
34 |
+
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. \
|
35 |
+
It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, \
|
36 |
+
restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of \
|
37 |
+
dialogue states and dialogue acts at both user and system sides.
|
38 |
+
"""
|
39 |
+
|
40 |
+
_HOMEPAGE = "https://github.com/thu-coai/CrossWOZ"
|
41 |
+
|
42 |
+
_LICENSE = "Apache License, Version 2.0"
|
43 |
+
|
44 |
+
_URLs = {
|
45 |
+
"train": "https://github.com/thu-coai/CrossWOZ/blob/master/data/crosswoz/train.json.zip",
|
46 |
+
"val": "https://github.com/thu-coai/CrossWOZ/blob/master/data/crosswoz/val.json.zip",
|
47 |
+
"test": "https://github.com/thu-coai/CrossWOZ/blob/master/data/crosswoz/test.json.zip"
|
48 |
+
}
|
49 |
+
|
50 |
+
|
51 |
+
class CrossWOZ(datasets.GeneratorBasedBuilder):
|
52 |
+
"""CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset"""
|
53 |
+
|
54 |
+
VERSION = datasets.Version("1.1.0")
|
55 |
+
|
56 |
+
def _info(self):
|
57 |
+
features = datasets.Features(
|
58 |
+
{
|
59 |
+
"gem_id": datasets.Value("string"),
|
60 |
+
"dialog_id": datasets.Value("string"),
|
61 |
+
"sys_id": datasets.Value("int32"),
|
62 |
+
"usr_id": datasets.Value("int32"),
|
63 |
+
"goal": datasets.Sequence(
|
64 |
+
{
|
65 |
+
"sub_goal_id": datasets.Value("int32"),
|
66 |
+
"domain": datasets.Value("string"),
|
67 |
+
"slot": datasets.Value("string"),
|
68 |
+
"value": datasets.Value("string"),
|
69 |
+
"has_mentioned": datasets.Value("bool"),
|
70 |
+
}
|
71 |
+
),
|
72 |
+
"task description": datasets.Value("string"),
|
73 |
+
"type": datasets.ClassLabel(names=["单领域", "独立多领域", "独立多领域+交通", "不独立多领域", "不独立多领域+交通"]),
|
74 |
+
"messages": datasets.Sequence(
|
75 |
+
{
|
76 |
+
"content": datasets.Value("string"),
|
77 |
+
"role": datasets.ClassLabel(names=["usr", "sys"]),
|
78 |
+
"dialog_act": datasets.Sequence(
|
79 |
+
{
|
80 |
+
"intent": datasets.Value("string"),
|
81 |
+
"domain": datasets.Value("string"),
|
82 |
+
"slot": datasets.Value("string"),
|
83 |
+
"value": datasets.Value("string"),
|
84 |
+
}
|
85 |
+
),
|
86 |
+
"user_state": datasets.Sequence(
|
87 |
+
{
|
88 |
+
"sub_goal_id": datasets.Value("int32"),
|
89 |
+
"domain": datasets.Value("string"),
|
90 |
+
"slot": datasets.Value("string"),
|
91 |
+
"value": datasets.Value("string"),
|
92 |
+
"has_mentioned": datasets.Value("bool"),
|
93 |
+
}
|
94 |
+
),
|
95 |
+
"sys_state": {
|
96 |
+
"景点": {
|
97 |
+
"名称": datasets.Value("string"),
|
98 |
+
"门票": datasets.Value("string"),
|
99 |
+
"游玩时间": datasets.Value("string"),
|
100 |
+
"评分": datasets.Value("string"),
|
101 |
+
"周边景点": datasets.Value("string"),
|
102 |
+
"周边餐馆": datasets.Value("string"),
|
103 |
+
"周边酒店": datasets.Value("string"),
|
104 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
105 |
+
},
|
106 |
+
"餐馆": {
|
107 |
+
"名称": datasets.Value("string"),
|
108 |
+
"推荐菜": "驴杂汤",
|
109 |
+
"人均消费": datasets.Value("string"),
|
110 |
+
"评分": datasets.Value("string"),
|
111 |
+
"周边景点": datasets.Value("string"),
|
112 |
+
"周边餐馆": datasets.Value("string"),
|
113 |
+
"周边酒店": datasets.Value("string"),
|
114 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
115 |
+
},
|
116 |
+
"酒店": {
|
117 |
+
"名称": datasets.Value("string"),
|
118 |
+
"酒店类型": datasets.Value("string"),
|
119 |
+
"酒店设施": datasets.Value("string"),
|
120 |
+
"价格": datasets.Value("string"),
|
121 |
+
"评分": datasets.Value("string"),
|
122 |
+
"周边景点": datasets.Value("string"),
|
123 |
+
"周边餐馆": datasets.Value("string"),
|
124 |
+
"周边酒店": datasets.Value("string"),
|
125 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
126 |
+
},
|
127 |
+
"地铁": {
|
128 |
+
"出发地": datasets.Value("string"),
|
129 |
+
"目的地": datasets.Value("string"),
|
130 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
131 |
+
},
|
132 |
+
"出租": {
|
133 |
+
"出发地": datasets.Value("string"),
|
134 |
+
"目的地": datasets.Value("string"),
|
135 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
136 |
+
}
|
137 |
+
},
|
138 |
+
"sys_state_init": {
|
139 |
+
"景点": {
|
140 |
+
"名称": datasets.Value("string"),
|
141 |
+
"门票": datasets.Value("string"),
|
142 |
+
"游玩时间": datasets.Value("string"),
|
143 |
+
"评分": datasets.Value("string"),
|
144 |
+
"周边景点": datasets.Value("string"),
|
145 |
+
"周边餐馆": datasets.Value("string"),
|
146 |
+
"周边酒店": datasets.Value("string"),
|
147 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
148 |
+
},
|
149 |
+
"餐馆": {
|
150 |
+
"名称": datasets.Value("string"),
|
151 |
+
"推荐菜": "驴杂汤",
|
152 |
+
"人均消费": datasets.Value("string"),
|
153 |
+
"评分": datasets.Value("string"),
|
154 |
+
"周边景点": datasets.Value("string"),
|
155 |
+
"周边餐馆": datasets.Value("string"),
|
156 |
+
"周边酒店": datasets.Value("string"),
|
157 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
158 |
+
},
|
159 |
+
"酒店": {
|
160 |
+
"名称": datasets.Value("string"),
|
161 |
+
"酒店类型": datasets.Value("string"),
|
162 |
+
"酒店设施": datasets.Value("string"),
|
163 |
+
"价格": datasets.Value("string"),
|
164 |
+
"评分": datasets.Value("string"),
|
165 |
+
"周边景点": datasets.Value("string"),
|
166 |
+
"周边餐馆": datasets.Value("string"),
|
167 |
+
"周边酒店": datasets.Value("string"),
|
168 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
169 |
+
},
|
170 |
+
"地铁": {
|
171 |
+
"出发地": datasets.Value("string"),
|
172 |
+
"目的地": datasets.Value("string"),
|
173 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
174 |
+
},
|
175 |
+
"出租": {
|
176 |
+
"出发地": datasets.Value("string"),
|
177 |
+
"目的地": datasets.Value("string"),
|
178 |
+
"selectedResults": datasets.Sequence(datasets.Value("string"))
|
179 |
+
}
|
180 |
+
},
|
181 |
+
}
|
182 |
+
),
|
183 |
+
"final_goal": datasets.Sequence(
|
184 |
+
{
|
185 |
+
"sub_goal_id": datasets.Value("int32"),
|
186 |
+
"domain": datasets.Value("string"),
|
187 |
+
"slot": datasets.Value("string"),
|
188 |
+
"value": datasets.Value("string"),
|
189 |
+
"has_mentioned": datasets.Value("bool"),
|
190 |
+
}
|
191 |
+
)
|
192 |
+
}
|
193 |
+
)
|
194 |
+
return datasets.DatasetInfo(
|
195 |
+
# This is the description that will appear on the datasets page.
|
196 |
+
description=_DESCRIPTION,
|
197 |
+
# This defines the different columns of the dataset and their types
|
198 |
+
features=features, # Here we define them above because they are different between the two configurations
|
199 |
+
# If there's a common (input, target) tuple from the features,
|
200 |
+
# specify them here. They'll be used if as_supervised=True in
|
201 |
+
# builder.as_dataset.
|
202 |
+
supervised_keys=None,
|
203 |
+
# Homepage of the dataset for documentation
|
204 |
+
homepage=_HOMEPAGE,
|
205 |
+
# License for the dataset if available
|
206 |
+
license=_LICENSE,
|
207 |
+
# Citation for the dataset
|
208 |
+
citation=_CITATION,
|
209 |
+
)
|
210 |
+
|
211 |
+
def _split_generators(self, dl_manager):
|
212 |
+
"""Returns SplitGenerators."""
|
213 |
+
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
|
214 |
+
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
|
215 |
+
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
216 |
+
downloaded_files = dl_manager.download_and_extract(_URLs)
|
217 |
+
return [
|
218 |
+
datasets.SplitGenerator(
|
219 |
+
name=datasets.Split.TRAIN,
|
220 |
+
# These kwargs will be passed to _generate_examples
|
221 |
+
gen_kwargs={
|
222 |
+
"filepath": os.path.join(downloaded_files["train"], "train.json"),
|
223 |
+
"split": "train",
|
224 |
+
},
|
225 |
+
),
|
226 |
+
datasets.SplitGenerator(
|
227 |
+
name=datasets.Split.TEST,
|
228 |
+
# These kwargs will be passed to _generate_examples
|
229 |
+
gen_kwargs={
|
230 |
+
"filepath": os.path.join(downloaded_files["test"], "test.json"),
|
231 |
+
"split": "test"
|
232 |
+
},
|
233 |
+
),
|
234 |
+
datasets.SplitGenerator(
|
235 |
+
name=datasets.Split.VALIDATION,
|
236 |
+
# These kwargs will be passed to _generate_examples
|
237 |
+
gen_kwargs={
|
238 |
+
"filepath": os.path.join(downloaded_files["val"], "val.json"),
|
239 |
+
"split": "dev",
|
240 |
+
},
|
241 |
+
),
|
242 |
+
]
|
243 |
+
|
244 |
+
def _generate_examples(
|
245 |
+
self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
246 |
+
):
|
247 |
+
""" Yields examples as (key, example) tuples. """
|
248 |
+
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
|
249 |
+
# The `key` is here for legacy reason (tfds) and is not important in itself.
|
250 |
+
def convert_goal(raw_goal):
|
251 |
+
goal = []
|
252 |
+
for subgoal in raw_goal:
|
253 |
+
goal.append({
|
254 |
+
"sub_goal_id": subgoal[0],
|
255 |
+
"domain": subgoal[1],
|
256 |
+
"slot": subgoal[2],
|
257 |
+
"value": str(subgoal[3]),
|
258 |
+
"has_mentioned": subgoal[4],
|
259 |
+
})
|
260 |
+
return goal
|
261 |
+
|
262 |
+
key = 0
|
263 |
+
with open(filepath, encoding="utf-8") as f:
|
264 |
+
data = json.load(f)
|
265 |
+
for dialog_id, dialog in data.items():
|
266 |
+
messages = []
|
267 |
+
for turn in dialog["messages"]:
|
268 |
+
dialog_act = []
|
269 |
+
for da in turn["dialog_act"]:
|
270 |
+
dialog_act.append({
|
271 |
+
"intent": da[0],
|
272 |
+
"domain": da[1],
|
273 |
+
"slot": da[2],
|
274 |
+
"value": da[3],
|
275 |
+
})
|
276 |
+
turn["dialog_act"] = dialog_act
|
277 |
+
if "user_state" not in turn:
|
278 |
+
turn["user_state"] = []
|
279 |
+
else:
|
280 |
+
turn["user_state"] = convert_goal(turn["user_state"])
|
281 |
+
if "sys_state" not in turn:
|
282 |
+
turn["sys_state"] = {}
|
283 |
+
if "sys_state_init" not in turn:
|
284 |
+
turn["sys_state_init"] = {}
|
285 |
+
messages.append(turn)
|
286 |
+
|
287 |
+
yield key, {
|
288 |
+
"gem_id": f"{self.config.name}-{split}-{key}",
|
289 |
+
"dialog_id": dialog_id,
|
290 |
+
"sys_id": dialog["sys-usr"][0],
|
291 |
+
"usr_id": dialog["sys-usr"][1],
|
292 |
+
"goal": convert_goal(dialog["goal"]),
|
293 |
+
"task description": dialog["task description"],
|
294 |
+
"type": dialog["type"],
|
295 |
+
"messages": messages
|
296 |
+
"final_goal": convert_goal(dialog["final_goal"])
|
297 |
+
}
|
298 |
+
key += 1
|