CrossWOZ.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.
"""CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset"""


import json
import os

import datasets


_CITATION = """\
@article{zhu2020crosswoz,
  author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang},
  title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2020}
}
"""

_DESCRIPTION = """\
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. \
It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, \
restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of \
dialogue states and dialogue acts at both user and system sides.
"""

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

_LICENSE = "Apache License, Version 2.0"


class CrossWOZ(datasets.GeneratorBasedBuilder):
    """CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset"""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "dialog_id": datasets.Value("string"),
                "sys_id": datasets.Value("int32"),
                "usr_id": datasets.Value("int32"),
                "goal": datasets.Sequence((datasets.Value("string"),)),
                "task description": datasets.Sequence(datasets.Value("string")),
                "type": datasets.Value("string"),
                "messages": datasets.Sequence(
                    {
                        "content": datasets.Value("string"),
                        "role": datasets.Value("string"),
                        "dialog_act": datasets.Sequence((datasets.Value("string"),)),
                        "user_state": datasets.Sequence((datasets.Value("string"),)),
                        "sys_state": {
                            "景点": {
                                "名称": datasets.Value("string"),
                                "门票": datasets.Value("string"),
                                "游玩时间": datasets.Value("string"),
                                "评分": datasets.Value("string"),
                                "周边景点": datasets.Value("string"),
                                "周边餐馆": datasets.Value("string"),
                                "周边酒店": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "餐馆": {
                                "名称": datasets.Value("string"),
                                "推荐菜": datasets.Value("string"),
                                "人均消费": datasets.Value("string"),
                                "评分": datasets.Value("string"),
                                "周边景点": datasets.Value("string"),
                                "周边餐馆": datasets.Value("string"),
                                "周边酒店": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "酒店": {
                                "名称": datasets.Value("string"),
                                "酒店类型": datasets.Value("string"),
                                "酒店设施": datasets.Value("string"),
                                "价格": datasets.Value("string"),
                                "评分": datasets.Value("string"),
                                "周边景点": datasets.Value("string"),
                                "周边餐馆": datasets.Value("string"),
                                "周边酒店": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "地铁": {
                                "出发地": datasets.Value("string"),
                                "目的地": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "出租": {
                                "出发地": datasets.Value("string"),
                                "目的地": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            }
                        },
                        "sys_state_init": {
                            "景点": {
                                "名称": datasets.Value("string"),
                                "门票": datasets.Value("string"),
                                "游玩时间": datasets.Value("string"),
                                "评分": datasets.Value("string"),
                                "周边景点": datasets.Value("string"),
                                "周边餐馆": datasets.Value("string"),
                                "周边酒店": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "餐馆": {
                                "名称": datasets.Value("string"),
                                "推荐菜": datasets.Value("string"),
                                "人均消费": datasets.Value("string"),
                                "评分": datasets.Value("string"),
                                "周边景点": datasets.Value("string"),
                                "周边餐馆": datasets.Value("string"),
                                "周边酒店": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "酒店": {
                                "名称": datasets.Value("string"),
                                "酒店类型": datasets.Value("string"),
                                "酒店设施": datasets.Value("string"),
                                "价格": datasets.Value("string"),
                                "评分": datasets.Value("string"),
                                "周边景点": datasets.Value("string"),
                                "周边餐馆": datasets.Value("string"),
                                "周边酒店": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "地铁": {
                                "出发地": datasets.Value("string"),
                                "目的地": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            },
                            "出租": {
                                "出发地": datasets.Value("string"),
                                "目的地": datasets.Value("string"),
                                "selectedResults": datasets.Sequence(datasets.Value("string"))
                            }
                        },
                    }
                ),
                "final_goal": datasets.Sequence((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,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # 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):
        """Returns SplitGenerators."""
        # 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("data.zip")
        return [
            # datasets.SplitGenerator(
            #     name=datasets.Split.TRAIN,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": os.path.join(data_dir, "train.json"),
            #         "split": "train",
            #     },
            # ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.TEST,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": os.path.join(data_dir, "test.json"),
            #         "split": "test"
            #     },
            # ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": os.path.join(data_dir, "val.json"),
            #         "split": "dev",
            #     },
            # ),
            # datasets.SplitGenerator(
            #     name="challenge.CM+T",
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": os.path.join(data_dir, "test.json"),
            #         "split": "challenge.CM+T",
            #     },
            # ),
            datasets.SplitGenerator(
                name="challenge.butterfingers",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract("challenge_butterfingers.jsonl"),
                    "split": "challenge",
                },
            ),
        ]

    def _generate_examples(
        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.
        def empty_sys_state():
            return {
                "景点": {
                    "名称": "",
                    "门票": "",
                    "游玩时间": "",
                    "评分": "",
                    "周边景点": "",
                    "周边餐馆": "",
                    "周边酒店": "",
                    "selectedResults": []
                },
                "餐馆": {
                    "名称": "",
                    "推荐菜": "",
                    "人均消费": "",
                    "评分": "",
                    "周边景点": "",
                    "周边餐馆": "",
                    "周边酒店": "",
                    "selectedResults": []
                },
                "酒店": {
                    "名称": "",
                    "酒店类型": "",
                    "酒店设施": "",
                    "价格": "",
                    "评分": "",
                    "周边景点": "",
                    "周边餐馆": "",
                    "周边酒店": "",
                    "selectedResults": []
                },
                "地铁": {
                    "出发地": "",
                    "目的地": "",
                    "selectedResults": []
                },
                "出租": {
                    "出发地": "",
                    "目的地": "",
                    "selectedResults": []
                }
            }

        if split == 'challenge':
            with open(filepath, encoding="utf-8") as f:
                for id_, row in enumerate(f):
                    data = json.loads(row)
                    yield id_, {
                        "sentence": data["sentence"],
                        "label": data["label"],
                        "gem_id": data["gem_id"]
                    }

        key = 0
        with open(filepath, encoding="utf-8") as f:
            data = json.load(f)
            for dialog_id, dialog in data.items():
                if split == "challenge.CM+T" and dialog["type"] != "不独立多领域+交通":
                    continue
                messages = []
                for turn in dialog["messages"]:
                    if "user_state" not in turn:
                        turn["user_state"] = []
                    else:
                        turn["user_state"] = list(map(tuple, turn["user_state"]))
                    if "sys_state" not in turn:
                        turn["sys_state"] = empty_sys_state()
                    if "sys_state_init" not in turn:
                        turn["sys_state_init"] = empty_sys_state()
                    messages.append(turn)

                yield key, {
                    "gem_id": f"GEM-CrossWOZ-{split}-{key}",
                    "dialog_id": dialog_id,
                    "sys_id": dialog["sys-usr"][0],
                    "usr_id": dialog["sys-usr"][1],
                    "goal": list(map(tuple, dialog["goal"])),
                    "task description": dialog["task description"],
                    "type": dialog["type"],
                    "messages": messages,
                    "final_goal": list(map(tuple, dialog["final_goal"]))
                }
                key += 1