Datasets:
kd_conv

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+---
+annotations_creators:
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+languages:
+- zh
+licenses:
+- apache-2-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- dialogue-modeling
+- other-multi-turn
+---
+# Dataset Card for KdConv
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+## Dataset Description
+- **Repository:** [Github](https://github.com/thu-coai/KdConv)
+- **Paper:** [{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://www.aclweb.org/anthology/2020.acl-main.635.pdf)
+### Dataset Summary
+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.
+### Supported Tasks and Leaderboards
+This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup.
+### Languages
+This dataset has only Chinese Language.
+## Dataset Structure
+### Data Instances
+Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking
+, e.g.:
+```
+{
+  "messages": [
+    {
+      "message": "对《我喜欢上你时的内心活动》这首歌有了解吗？"
+    },
+    {
+      "attrs": [
+        {
+          "attrname": "Information",
+          "attrvalue": "《我喜欢上你时的内心活动》是由韩寒填词，陈光荣作曲，陈绮贞演唱的歌曲，作为电影《喜欢你》的主题曲于2017年4月10日首发。2018年，该曲先后提名第37届香港电影金像奖最佳原创电影歌曲奖、第7届阿比鹿音乐奖流行单曲奖。",
+          "name": "我喜欢上你时的内心活动"
+        }
+      ],
+      "message": "有些了解，是电影《喜欢你》的主题曲。"
+    },
+    ...
+    {
+      "attrs": [
+        {
+          "attrname": "代表作品",
+          "attrvalue": "旅行的意义",
+          "name": "陈绮贞"
+        },
+        {
+          "attrname": "代表作品",
+          "attrvalue": "时间的歌",
+          "name": "陈绮贞"
+        }
+      ],
+      "message": "我还知道《旅行的意义》与《时间的歌》，都算是她的代表作。"
+    },
+    {
+      "message": "好，有时间我找出来听听。"
+    }
+  ],
+  "name": "我喜欢上你时的内心活动"
+}
+```
+The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
+, relationship, tail entity), e.g.:
+```
+"忽然之间": [
+  [
+    "忽然之间",
+    "Information",
+    "《忽然之间》是歌手 莫文蔚演唱的歌曲，由 周耀辉， 李卓雄填词， 林健华谱曲，收录在莫文蔚1999年发行专辑《 就是莫文蔚》里。"
+  ],
+  [
+    "忽然之间",
+    "谱曲",
+    "林健华"
+  ]
+  ...
+]
+```
+### Data Fields
+Conversation data fields:
+- `name`: the starting topic (entity) of the conversation
+- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
+- `messages`:  list of all the turns in the dialogue. For each turn:
+    - `message`: the utterance
+    - `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet:
+        - `name`: the head entity
+        - `attrname`: the relation
+        - `attrvalue`: the tail entity
+Knowledge Base data fields:
+- `head_entity`: the head entity
+- `kb_triplets`: list of corresponding triplets
+- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
+### Data Splits
+The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes:
+|                            | train  | dev    | test |
+| -----                      | ------ | -----  | ---- |
+| travel                     | 1200   | 1200   | 1200 |
+| film                       | 1200   | 150    | 150  |
+| music                      | 1200   | 150    | 150  |
+| all                        | 3600   | 450    | 450  |
+The Knowledge base dataset is having only train split with following sizes:
+|           | train  |
+| -----     | ------ |
+| travel    | 1154   |
+| film      | 8090   |
+| music     | 4441   |
+| all       | 13685   |
+## Dataset Creation
+### Curation Rationale
+[More Information Needed]
+### Source Data
+#### Initial Data Collection and Normalization
+[More Information Needed]
+#### Who are the source language producers?
+[More Information Needed]
+### Annotations
+#### Annotation process
+[More Information Needed]
+#### Who are the annotators?
+[More Information Needed]
+### Personal and Sensitive Information
+[More Information Needed]
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed]
+### Discussion of Biases
+[More Information Needed]
+### Other Known Limitations
+[More Information Needed]
+## Additional Information
+### Dataset Curators
+[More Information Needed]
+### Licensing Information
+Apache License 2.0
+### Citation Information
+```
+@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",
+}
+```

dataset_infos.json ADDED Viewed

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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.
+"""KdConv: Chinese multi-domain Knowledge-driven Conversionsation dataset"""
+from __future__ import absolute_import, division, print_function
+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}