Datasets:
kd_conv

	# 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.
	"""KdConv: Chinese multi-domain Knowledge-driven Conversation dataset"""


	import json
	import os

	import datasets


	_CITATION = """\
	@inproceedings{zhou-etal-2020-kdconv,
	title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
	author = "Zhou, Hao and
	Zheng, Chujie and
	Huang, Kaili and
	Huang, Minlie and
	Zhu, Xiaoyan",
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.acl-main.635",
	doi = "10.18653/v1/2020.acl-main.635",
	pages = "7098--7108",
	}
	"""


	_DESCRIPTION = """\
	KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn \
	conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), \
	and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related \
	topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer \
	learning and domain adaptation.\
	"""


	_HOMEPAGE = "https://github.com/thu-coai/KdConv"


	_LICENSE = "Apache License 2.0"


	_URL = "data.zip"

	_DOMAINS = ["travel", "music", "film"]
	_DATA_TYPES = ["dialogues", "knowledge_base"]


	class KdConv(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.1.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=domain + "_" + type,
	description="This part of dataset covers {0} domain and {1} data " "of the corpus".format(domain, type),
	)
	for domain in _DOMAINS
	for type in _DATA_TYPES
	] + [
	datasets.BuilderConfig(
	name="all_" + type,
	description="This part of dataset covers all domains and {0} data of " "the corpus".format(type),
	)
	for type in _DATA_TYPES
	]

	DEFAULT_CONFIG_NAME = "all_dialogues"

	def _info(self):
	if "dialogues" in self.config.name:
	features = datasets.Features(
	{
	"messages": datasets.Sequence(
	{
	"message": datasets.Value("string"),
	"attrs": datasets.Sequence(
	{
	"attrname": datasets.Value("string"),
	"attrvalue": datasets.Value("string"),
	"name": datasets.Value("string"),
	}
	),
	}
	),
	"name": datasets.Value("string"),
	"domain": datasets.Value("string"),
	}
	)
	else:
	features = datasets.Features(
	{
	"head_entity": datasets.Value("string"),
	"kb_triplets": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	"domain": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	data_dir = dl_manager.download_and_extract(_URL)
	base_dir = os.path.join(data_dir, "data")
	if "dialogues" in self.config.name:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data_dir": base_dir,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"data_dir": base_dir, "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"data_dir": base_dir,
	"split": "dev",
	},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data_dir": base_dir,
	"split": "train",
	},
	),
	]

	def _generate_examples(self, data_dir, split):
	"""Yields examples."""
	if "dialogues" in self.config.name:
	if "all" in self.config.name:
	file_dict = {
	domain: os.path.join(os.path.join(data_dir, domain), split + ".json") for domain in _DOMAINS
	}
	else:
	domain = self.config.name.split("_")[0]
	file_dict = {domain: os.path.join(os.path.join(data_dir, domain), split + ".json")}
	id_ = -1
	for domain, filepath in file_dict.items():
	with open(filepath, encoding="utf-8") as f:
	conversations = json.load(f)
	for conversation in conversations:
	id_ += 1
	conversation["domain"] = domain
	for turn in conversation["messages"]:
	if "attrs" in turn:
	attrnames = [kb_triplet.get("attrname", "") for kb_triplet in turn["attrs"]]
	attrvalues = [kb_triplet.get("attrvalue", "") for kb_triplet in turn["attrs"]]
	names = [kb_triplet.get("name", "") for kb_triplet in turn["attrs"]]
	else:
	attrnames, attrvalues, names = [], [], []
	turn["attrs"] = {"attrname": attrnames, "attrvalue": attrvalues, "name": names}

	yield id_, conversation
	else:
	if "all" in self.config.name:
	file_dict = {
	domain: os.path.join(os.path.join(data_dir, domain), "kb_" + domain + ".json")
	for domain in _DOMAINS
	}
	else:
	domain = self.config.name.split("_")[0]
	file_dict = {domain: os.path.join(os.path.join(data_dir, domain), "kb_" + domain + ".json")}

	id_ = -1
	for domain, filepath in file_dict.items():
	with open(filepath, encoding="utf-8") as f:
	kb_dict = json.load(f)
	for head_entity, kb_triplets in kb_dict.items():
	id_ += 1
	yield id_, {"head_entity": head_entity, "kb_triplets": kb_triplets, "domain": domain}