Create jmultiwoz.py

jmultiwoz.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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""JMultiWOZ: Japanese Multi-Domain Wizard-of-Oz dataset for task-oriented dialogue modelling"""
+
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{ohashi-etal-2024-jmultiwoz,
+    title = "JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset",
+    author = "Ohashi, Atsumoto and Hirai, Ryu and Iizuka, Shinya and Higashinaka, Ryuichiro",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation",
+    year = "2024",
+    url = "",
+    pages = "",
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+JMultiWOZ is a large-scale Japanese multi-domain task-oriented dialogue dataset. The dataset is collected using 
+the Wizard-of-Oz (WoZ) methodology, where two human annotators simulate the user and the system. The dataset contains 
+4,264 dialogues across 6 domains, including restaurant, hotel, attraction, shopping, taxi, and weather. 
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/nu-dialogue/jmultiwoz"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "CC BY-ND 4.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "original_zip": "https://github.com/ohashi56225/jmultiwoz-evaluation/blob/master/dataset/JMultiWOZ_1.0.zip",
+}
+
+
+def _flatten_value(values):
+    if not isinstance(values, list):
+        return values
+    flat_values = [
+        _flatten_value(v) if isinstance(v, list) else v for v in values
+    ]
+    return "[" + ", ".join(flat_values) + "]"
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class JMultiWOZDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features({
+            "dialogue_id": datasets.Value("int32"),
+            "dialogue_name": datasets.Value("string"),
+            "system_name": datasets.Value("string"),
+            "user_name": datasets.Value("string"),
+            "goal": datasets.Sequence({
+                "domain": datasets.Value("string"),
+                "task": datasets.Value("string"),
+                "slot": datasets.Value("string"),
+                "value": datasets.Value("string"),
+            }),
+            "goal_description": datasets.Sequence({
+                "domain": datasets.Value("string"),
+                "text": datasets.Value("string"),
+            }),
+            "turns": datasets.Sequence({
+                "turn_id": datasets.Value("int32"),
+                "speaker": datasets.Value("string"),
+                "utterance": datasets.Value("string"),
+                "dialogue_state": {
+                    "belief_state": datasets.Sequence({
+                        "domain": datasets.Value("string"),
+                        "slot": datasets.Value("string"),
+                        "value": datasets.Value("string"),
+                    }),
+                    "book_state": datasets.Sequence({
+                        "domain": datasets.Value("string"),
+                        "slot": datasets.Value("string"),
+                        "value": datasets.Value("string"),
+                    }),
+                    "db_result": datasets.Sequence({
+                        "candidate_entities": datasets.Sequence(datasets.Value("string")),
+                        "active_entity": datasets.Sequence({
+                            "slot": datasets.Value("string"),
+                            "value": datasets.Value("string"),
+                        })
+                    }),
+                    "book_result": datasets.Sequence({
+                        "domain": datasets.Value("string"),
+                        "success": datasets.Value("string"),
+                        "ref": 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, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # 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):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # 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
+        data_dir = dl_manager.download_and_extract(_URLS["original_zip"])
+        split_list_path = os.path.join(data_dir, "JMultiWOZ_1.0/split_list.json")
+        dialogues_path = os.path.join(data_dir, "JMultiWOZ_1.0/dialogues.json")
+        with open(split_list_path, "r", encoding="utf-8") as f:
+            split_list = json.load(f)
+        with open(dialogues_path, "r", encoding="utf-8") as f:
+            dialogues = json.load(f)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["train"]],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["dev"]],
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["test"]],
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, dialogues, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        
+        for id_, dialogue in enumerate(dialogues):
+            example = {
+                "dialogue_id": dialogue["dialogue_id"],
+                "dialogue_name": dialogue["dialogue_name"],
+                "system_name": dialogue["system_name"],
+                "user_name": dialogue["user_name"],
+                "goal": [],
+                "goal_description": [],
+                "turns": [],
+            }
+
+            for domain, tasks in dialogue["goal"].items():
+                for task, slot_values in tasks.items():
+                    if task == "reqt":
+                        slot_values = {slot: None for slot in slot_values}
+                    for slot, value in slot_values.items():
+                        example["goal"].append({
+                            "domain": domain,
+                            "task": task,
+                            "slot": slot,
+                            "value": value,
+                        })
+            
+            for domain, texts in dialogue["goal_description"].items():
+                for text in texts:
+                    example["goal_description"].append({
+                        "domain": domain,
+                        "text": text,
+                    })
+            
+            for turn in dialogue["turns"]:
+                example_turn = {
+                    "turn_id": turn["turn_id"],
+                    "speaker": turn["speaker"],
+                    "utterance": turn["utterance"],
+                    "dialogue_state": {
+                        "belief_state": [],
+                        "book_state": [],
+                        "db_result": [],
+                        "book_result": [],
+                    },
+                }
+                if turn["speaker"] == "USER":
+                    continue
+
+                for domain, slots in turn["dialogue_state"]["belief_state"].items():
+                    for slot, value in slots.items():
+                        example_turn["dialogue_state"]["belief_state"].append({
+                            "domain": domain,
+                            "slot": slot,
+                            "value": value,
+                        })
+
+                for domain, slots in turn["dialogue_state"]["book_state"].items():
+                    for slot, value in slots.items():
+                        example_turn["dialogue_state"]["book_state"].append({
+                            "domain": domain,
+                            "slot": slot,
+                            "value": value,
+                        })
+                
+                candidate_entities = turn["dialogue_state"]["db_result"]["candidate_entities"]
+                active_entity = turn["dialogue_state"]["db_result"]["active_entity"]
+                if not active_entity:
+                    active_entity = {}
+                example_turn["dialogue_state"]["db_result"].append({
+                    "candidate_entities":candidate_entities,
+                    "active_entity": [{
+                        "slot": slot,
+                        "value": _flatten_value(value),
+                    } for slot, value in active_entity.items()]
+                })
+                
+                for domain, result in turn["dialogue_state"]["book_result"].items():
+                    example_turn["dialogue_state"]["book_result"].append({
+                        "domain": domain,
+                        "success": result["success"],
+                        "ref": result["ref"],
+                    })
+                
+                example["turns"].append(example_turn)
+
+            yield id_, example