:page_facing_up: Add a loading script

multi-relational-multi-party-chat-corpus.py

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+# 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.
+
+
+"""
+Multi-Relational Multi-Party Chat Corpus (MRMP): Japanese text-based chats comprising first-time-meeting dialogues and family-included dialogues
+
+This script is based on
+https://github.com/huggingface/datasets/blob/d69d1c654c4645a0474731794a20d4c012d2d214/templates/new_dataset_script.py
+"""
+
+
+import json
+import re
+from pathlib import Path
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{tsuda-etal-2025-multi-relational-multi-party-chat-corpus,
+title = "Constructing a Multi-Party Conversational Corpus Focusing on Interlocutor Relationships",
+    author = "Tsuda, Taro and
+      Yamashita, Sanae and
+      Inoue, Koji and
+      Kawahara, Tatsuya
+      and Higashinaka, Ryuichiro",
+    booktitle="Proceedings of the 29th Workshop on the Semantics and Pragmatics of Dialogue",
+    year = "2025",
+    pages = "193--202"
+}
+
+@inproceedings{tsuda-etal-2025-multi-relational-multi-party-chat-corpus-ja,
+    title = "{M}ulti-{R}elational {M}ulti-{P}arty {C}hat {C}orpus: 話者間の関係性に着目したマルチパーティ雑談対話コーパス",
+    author = "津田 太郎 and 山下 紗苗 and 井上 昂治 and 河原 達也 and 東中 竜一郎",
+    booktitle = "言語処理学会第31回年次大会発表論文集",
+    year = "2025",
+    pages = "4011--4016"
+}
+"""
+
+_DESCRIPTION = """\
+Multi-Relational Multi-Party Chat Corpus (MRMP): Japanese text-based chats comprising first-time-meeting dialogues and family-included dialogues
+"""
+
+_HOMEPAGE = "https://github.com/nu-dialogue/multi-relational-multi-party-chat-corpus/"
+
+_LICENSE = "CC BY-ND 4.0"
+
+_VERSION = "1.0.0"
+
+_URL = f"https://github.com/nu-dialogue/multi-relational-multi-party-chat-corpus/archive/refs/tags/v{_VERSION}.zip"
+
+
+class MultiRelationalMultiPartyChatCorpus(datasets.GeneratorBasedBuilder):
+    """MultiRelationalMultiPartyChatCorpus consists of dialogues and interlocutor information."""
+
+    VERSION = datasets.Version(_VERSION)
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="dialogue",
+            version=VERSION,
+            description="This part contains dialogues"
+            ),
+        datasets.BuilderConfig(
+            name="interlocutor",
+            version=VERSION,
+            description="This part contains interlocutor information"
+            )
+    ]
+
+    DEFAULT_CONFIG_NAME = "dialogue"
+
+    def _info(self):
+        if self.config.name == "dialogue":
+            features = datasets.Features(
+                    {
+                        "dialogue_id": datasets.Value("string"),
+                        "dialogue_type": datasets.ClassLabel(names=["First time", "Family"]),
+                        "interlocutors": datasets.Sequence(datasets.Value("string"), length=3),
+                        "relationship": datasets.Sequence(datasets.Value("string")),
+                        "utterances": datasets.Sequence(
+                            {
+                                "utterance_id": datasets.Value("int32"),
+                                "interlocutor_id": datasets.Value("string"),
+                                "text": datasets.Value("string"),
+                                "mention_to": datasets.Sequence(datasets.Value("string")),
+                                }
+                            ),
+                        "evaluations": datasets.Sequence(
+                            {
+                                "interlocutor_id": datasets.Value("string"),
+                                "informativeness": datasets.Value("int32"),
+                                "comprehension": datasets.Value("int32"),
+                                "familiarity": datasets.Value("int32"),
+                                "interest": datasets.Value("int32"),
+                                "proactiveness": datasets.Value("int32"),
+                                "satisfaction": datasets.Value("int32")
+                                }
+                            )
+                        }
+                    )
+
+        elif self.config.name == "interlocutor":
+            features = datasets.Features(
+                    {
+                        "interlocutor_id": datasets.Value("string"),
+                        "persona": datasets.Sequence(datasets.Value("string"), length=10),
+                        "personality": {
+                            "BigFive_Openness": datasets.Value("float32"),
+                            "BigFive_Conscientiousness": datasets.Value("float32"),
+                            "BigFive_Extraversion": datasets.Value("float32"),
+                            "BigFive_Agreeableness": datasets.Value("float32"),
+                            "BigFive_Neuroticism": datasets.Value("float32"),
+                            "KiSS18_BasicSkill": datasets.Value("float32"),
+                            "KiSS18_AdvancedSkill": datasets.Value("float32"),
+                            "KiSS18_EmotionalManagementSkill": datasets.Value("float32"),
+                            "KiSS18_OffenceManagementSkill": datasets.Value("float32"),
+                            "KiSS18_StressManagementSkill": datasets.Value("float32"),
+                            "KiSS18_PlanningSkill": datasets.Value("float32"),
+                            "IOS": datasets.Value("int32"),
+                            "ATQ_Fear": datasets.Value("float32"),
+                            "ATQ_Frustration": datasets.Value("float32"),
+                            "ATQ_Sadness": datasets.Value("float32"),
+                            "ATQ_Discomfort": datasets.Value("float32"),
+                            "ATQ_ActivationControl": datasets.Value("float32"),
+                            "ATQ_AttentionalControl": datasets.Value("float32"),
+                            "ATQ_InhibitoryControl": datasets.Value("float32"),
+                            "ATQ_Sociability": datasets.Value("float32"),
+                            "ATQ_HighIntensityPleasure": datasets.Value("float32"),
+                            "ATQ_PositiveAffect": datasets.Value("float32"),
+                            "ATQ_NeutralPerceptualSensitivity": datasets.Value("float32"),
+                            "ATQ_AffectivePerceptualSensitivity": datasets.Value("float32"),
+                            "ATQ_AssociativeSensitivity": datasets.Value("float32"),
+                            "SMS_Extraversion": datasets.Value("float32"),
+                            "SMS_OtherDirectedness": datasets.Value("float32"),
+                            "SMS_Acting": datasets.Value("float32"),
+                            },
+                        "demographic_information": {
+                            "gender": datasets.ClassLabel(names=["Male", "Female", "Other"]),
+                            "age": datasets.ClassLabel(names=["-19", "20-29", "30-39", "40-49", "50-59", "60-69"]),
+                            "education": datasets.ClassLabel(names=["High school graduate", "Two-year college", "Four-year college", "Postgraduate", "Other"]),
+                            "employment_status": datasets.ClassLabel(names=["Employed", "Homemaker", "Student", "Retired", "Unable to work", "None"]),
+                            "region_of_residence": datasets.ClassLabel(names=["Hokkaido", "Aomori", "Iwate", "Miyagi", "Akita", "Yamagata", "Fukushima", "Ibaraki", "Tochigi", "Gunma", "Saitama", "Chiba", "Tokyo", "Kanagawa", "Niigata", "Toyama", "Ishikawa", "Fukui", "Yamanashi", "Nagano", "Gifu", "Shizuoka", "Aichi", "Mie", "Shiga", "Kyoto", "Osaka", "Hyogo", "Nara", "Wakayama", "Tottori", "Shimane", "Okayama", "Hiroshima", "Yamaguchi", "Tokushima", "Kagawa", "Ehime", "Kochi", "Fukuoka", "Saga", "Nagasaki", "Kumamoto", "Oita", "Miyazaki", "Kagoshima", "Okinawa"]),
+                            },
+                        "text_chat_experience": {
+                            "age_of_first_chat": datasets.ClassLabel(names=["-9", "10-19", "20-29", "30-39", "40-49", "50-59"]),
+                            "frequency": datasets.ClassLabel(names=["Every day", "Once every few days", "Once a week", "Less frequent than these"]),
+                            "chatting_partners": datasets.Sequence(datasets.ClassLabel(names=["Family", "Friend", "Colleague", "Other"])),
+                            "typical_chat_content": datasets.Value("string"),
+                            },
+                        "pair_information": {
+                            "pair_flag": datasets.Value("bool"),
+                            "relationship": datasets.Sequence(datasets.ClassLabel(names=["Frequent acquaintance", "Someone known for years", "Someone engaged in joint activities"])),
+                            "relationship_detail": datasets.Value("string"),
+                            "pair_interlocutor_id": datasets.Value("string"),
+                            }
+                        }
+                    )
+
+        else:
+            raise ValueError(f"Config name `{self.config.name}` is invalid.")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+
+        if self.config.name == "dialogue":
+            filepath_list = Path(data_dir, f"multi-relational-multi-party-chat-corpus-{_VERSION}", "multi_relational_multi_party_chat_corpus", "dialogues").glob("**/*.json")
+            filepath_list = list(sorted(filepath_list, key=lambda x: self._natural_keys(x.stem)))
+
+        elif self.config.name == "interlocutor":
+            filepath_list = Path(data_dir, f"multi-relational-multi-party-chat-corpus-{_VERSION}", "multi_relational_multi_party_chat_corpus").glob("interlocutors.json")
+
+        else:
+            raise ValueError(f"Config name `{self.config.name}` is invalid.")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath_list": filepath_list
+                }
+            )
+        ]
+
+    def _generate_examples(self, filepath_list):
+        if self.config.name == "dialogue":
+            for filepath in filepath_list:
+                key = filepath.stem
+
+                with open(filepath, encoding="utf-8") as f:
+                    example = json.load(f)
+
+                yield key, example
+
+        elif self.config.name == "interlocutor":
+            for filepath in filepath_list:
+                with open(filepath, encoding="utf-8") as f:
+                    interlocutors = json.load(f)
+
+                for key, example in interlocutors.items():
+                    yield key, example
+
+        else:
+            raise ValueError(f"Config name `{self.config.name}` is invalid.")
+
+    def _atoi(self, text):
+        return int(text) if text.isdigit() else text
+
+    def _natural_keys(self, text):
+        return [self._atoi(c) for c in re.split(r'(\d+)', text)]