kd_conv / kd_conv.py
# 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 Conversionsation 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 = "https://github.com/thu-coai/KdConv/archive/master.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(os.path.join(data_dir, "KdConv-master"), "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}