RiSAWOZ / RiSAWOZ.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.
"""RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling"""
import json
import os
from typing import Dict
import datasets
_CITATION = """\
@inproceedings{quan-etal-2020-risawoz,
title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
author = "Quan, Jun and
Zhang, Shian and
Cao, Qian and
Li, Zizhong and
Xiong, Deyi",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.67",
pages = "930--940",
}
"""
_DESCRIPTION = """\
RiSAWOZ contains 11.2K human-to-human (H2H) multiturn semantically annotated dialogues, \
with more than 150K utterances spanning over 12 domains, \
which is larger than all previous annotated H2H conversational datasets.\
Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively.
"""
_HOMEPAGE = "https://github.com/terryqj0107/RiSAWOZ"
_LICENSE = "Attribution 4.0 International (CC BY 4.0) license."
_EMPTY_BELIEF_STATE = [
"旅游景点-名称",
"旅游景点-区域",
"旅游景点-景点类型",
"旅游景点-最适合人群",
"旅游景点-消费",
"旅游景点-是否地铁直达",
"旅游景点-门票价格",
"旅游景点-电话号码",
"旅游景点-地址",
"旅游景点-评分",
"旅游景点-开放时间",
"旅游景点-特点",
"餐厅-名称",
"餐厅-区域",
"餐厅-菜系",
"餐厅-价位",
"餐厅-是否地铁直达",
"餐厅-人均消费",
"餐厅-地址",
"餐厅-电话号码",
"餐厅-评分",
"餐厅-营业时间",
"餐厅-推荐菜",
"酒店-名称",
"酒店-区域",
"酒店-星级",
"酒店-价位",
"酒店-酒店类型",
"酒店-房型",
"酒店-停车场",
"酒店-房费",
"酒店-地址",
"酒店-电话号码",
"酒店-评分",
"电脑-品牌",
"电脑-产品类别",
"电脑-分类",
"电脑-内存容量",
"电脑-屏幕尺寸",
"电脑-CPU",
"电脑-价格区间",
"电脑-系列",
"电脑-商品名称",
"电脑-系统",
"电脑-游戏性能",
"电脑-CPU型号",
"电脑-裸机重量",
"电脑-显卡类别",
"电脑-显卡型号",
"电脑-特性",
"电脑-色系",
"电脑-待机时长",
"电脑-硬盘容量",
"电脑-价格",
"火车-出发地",
"火车-目的地",
"火车-日期",
"火车-车型",
"火车-坐席",
"火车-车次信息",
"火车-时长",
"火车-出发时间",
"火车-到达时间",
"火车-票价",
"飞机-出发地",
"飞机-目的地",
"飞机-日期",
"飞机-舱位档次",
"飞机-航班信息",
"飞机-起飞时间",
"飞机-到达时间",
"飞机-票价",
"飞机-准点率",
"天气-城市",
"天气-日期",
"天气-天气",
"天气-温度",
"天气-风力风向",
"天气-紫外线强度",
"电影-制片国家/地区",
"电影-类型",
"电影-年代",
"电影-主演",
"电影-导演",
"电影-片名",
"电影-主演名单",
"电影-具体上映时间",
"电影-片长",
"电影-豆瓣评分",
"电视剧-制片国家/地区",
"电视剧-类型",
"电视剧-年代",
"电视剧-主演",
"电视剧-导演",
"电视剧-片名",
"电视剧-主演名单",
"电视剧-首播时间",
"电视剧-集数",
"电视剧-单集片长",
"电视剧-豆瓣评分",
"辅导班-班号",
"辅导班-难度",
"辅导班-科目",
"辅导班-年级",
"辅导班-区域",
"辅导班-校区",
"辅导班-上课方式",
"辅导班-开始日期",
"辅导班-结束日期",
"辅导班-每周",
"辅导班-上课时间",
"辅导班-下课时间",
"辅导班-时段",
"辅导班-课次",
"辅导班-课时",
"辅导班-教室地点",
"辅导班-教师",
"辅导班-价格",
"辅导班-课程网址",
"辅导班-教师网址",
"汽车-名称",
"汽车-车型",
"汽车-级别",
"汽车-座位数",
"汽车-车身尺寸(mm)",
"汽车-厂商",
"汽车-能源类型",
"汽车-发动机排量(L)",
"汽车-发动机马力(Ps)",
"汽车-驱动方式",
"汽车-综合油耗(L/100km)",
"汽车-环保标准",
"汽车-驾驶辅助影像",
"汽车-巡航系统",
"汽车-价格(万元)",
"汽车-车系",
"汽车-动力水平",
"汽车-油耗水平",
"汽车-倒车影像",
"汽车-定速巡航",
"汽车-座椅加热",
"汽车-座椅通风",
"汽车-所属价格区间",
"医院-名称",
"医院-等级",
"医院-类别",
"医院-性质",
"医院-区域",
"医院-地址",
"医院-电话",
"医院-挂号时间",
"医院-门诊时间",
"医院-公交线路",
"医院-地铁可达",
"医院-地铁线路",
"医院-重点科室",
"医院-CT",
"医院-3.0T MRI",
"医院-DSA",
"通用-产品类别",
"火车-舱位档次",
"通用-系列",
"通用-价格区间",
"通用-品牌"
]
class RiSAWOZ(datasets.GeneratorBasedBuilder):
"""RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling"""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"dialogue_id": datasets.Value("string"),
"goal": datasets.Value("string"),
"domains": datasets.Sequence(datasets.Value("string")),
"dialogue": datasets.Sequence(
{
"turn_id": datasets.Value("int32"),
"turn_domain": datasets.Sequence(datasets.Value("string")),
"user_utterance": datasets.Value("string"),
"system_utterance": datasets.Value("string"),
"belief_state": {
"inform slot-values": {
d: datasets.Value("string") for d in _EMPTY_BELIEF_STATE
},
# "inform slot-values": datasets.Value("string"),
"turn_inform": {
d: datasets.Value("string") for d in _EMPTY_BELIEF_STATE
},
"turn request": datasets.Sequence(datasets.Value("string")),
},
"user_actions": datasets.Sequence(
datasets.Sequence(datasets.Value("string"))
),
"system_actions": datasets.Sequence(
datasets.Sequence(datasets.Value("string"))
),
"db_results": datasets.Sequence(datasets.Value("string")),
"segmented_user_utterance": datasets.Value("string"),
"segmented_system_utterance": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
# 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.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
_URL = {"train": "train.json", "test": "test.json", "dev": "dev.json"}
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": data_dir["test"], "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir["dev"],
"split": "dev",
},
),
]
def _generate_examples(
self,
filepath,
split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
"""Yields examples as (key, example) tuples."""
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
with open(filepath, encoding="utf-8") as f:
all_data = json.load(f)
id_ = 0
for data in all_data:
for slot in _EMPTY_BELIEF_STATE:
for dia in data["dialogue"]:
if slot not in dia["belief_state"]["inform slot-values"]:
dia["belief_state"]["inform slot-values"][slot] = ""
if slot not in dia["belief_state"]["turn_inform"]:
dia["belief_state"]["turn_inform"][slot] = ""
yield id_, {
"dialogue_id": data["dialogue_id"],
"goal": data["goal"],
"domains": data["domains"],
"dialogue": data["dialogue"],
}
id_ += 1