Datasets:
Languages:
English
Size:
1M<n<10M
ArXiv:
Tags:
task-oriented-dialog
task-oriented-dialogues
dialog-flow
dialog-modeling
dialogue-flow
dialogue-modeling
License:
| """ | |
| Copyright (c) 2024, Idiap Research Institute. | |
| All rights reserved. | |
| SPDX-License-Identifier: MIT License | |
| For full license text, see the LICENSE file in the repo root | |
| """ | |
| #!/usr/bin/env python3 | |
| import os | |
| import csv | |
| import json | |
| import datasets | |
| from datasets import (GeneratorBasedBuilder, | |
| BuilderConfig, | |
| SplitGenerator, | |
| DatasetInfo, | |
| Features, | |
| Value, | |
| Version) | |
| logger = datasets.logging.get_logger(__name__) | |
| datasets.logging.disable_progress_bar() | |
| _VERSION = Version("1.0.0") | |
| _CITATION = """ | |
| @inproceedings{burdisso-etal-2024-dialog2flow, | |
| title = "Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction", | |
| author = "Burdisso, Sergio and | |
| Madikeri, Srikanth and | |
| Motlicek, Petr", | |
| booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", | |
| month = nov, | |
| year = "2024", | |
| address = "Miami", | |
| publisher = "Association for Computational Linguistics", | |
| } | |
| """ | |
| DATASETS_PRETRAIN = ["dialog-acts", "slots", "dialog-acts+slots"] | |
| DATASETS_DS = { | |
| 'ABCD': ['test', 'train', 'val'], | |
| 'BiTOD': ['test', 'train', 'val'], | |
| 'DSTC2-Clean': ['test', 'train', 'val'], | |
| 'Disambiguation': ['test', 'train', 'val'], | |
| 'FRAMES': ['test', 'train'], | |
| 'HDSA-Dialog': ['test', 'train', 'val'], | |
| 'GECOR': ['train'], | |
| 'KETOD': ['test', 'train', 'val'], | |
| 'MS-DC': ['train'], | |
| 'MULTIWOZ2_2': ['test', 'train', 'val'], | |
| 'MulDoGO': ['test', 'train', 'val'], | |
| 'MultiWOZ_2.1': ['test', 'train', 'val'], | |
| 'SGD': ['test', 'train', 'val'], | |
| 'SimJointMovie': ['test', 'train', 'val'], | |
| 'SimJointRestaurant': ['test', 'train', 'val'], | |
| 'Taskmaster1': ['test', 'train', 'val'], | |
| 'Taskmaster2': ['train'], | |
| 'Taskmaster3': ['test', 'train', 'val'], | |
| 'WOZ2_0': ['test', 'train', 'val'], | |
| 'SimJointGEN': ['test', 'train', 'val'], | |
| } | |
| DATASETS = list(DATASETS_DS.keys()) + DATASETS_PRETRAIN | |
| SPLIT2NAME = { | |
| "train": datasets.Split.TRAIN, | |
| "val": datasets.Split.VALIDATION, | |
| "test": datasets.Split.TEST, | |
| } | |
| class Dialog2FlowConfig(BuilderConfig): | |
| """BuilderConfig for Dialog2Flow.""" | |
| def __init__(self, name, citation, url, **kwargs): | |
| """BuilderConfig for Dialog2Flow. | |
| Args: | |
| extra_features: `list[string]`, list of the features that will appear in the | |
| feature dict. Should not include "label". | |
| data_url: `string`, url to download the zip file from. | |
| citation: `string`, citation for the data set. | |
| url: `string`, url for information about the data set. | |
| label_classes: `list[string]`, the list of classes for the label if the | |
| label is present as a string. Non-string labels will be cast to either | |
| 'False' or 'True'. | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(Dialog2FlowConfig, self).__init__(version=_VERSION, **kwargs) | |
| self.name = name | |
| self.citation = citation | |
| self.url = url | |
| class Dialog2FlowBuilder(GeneratorBasedBuilder): | |
| BUILDER_CONFIG_CLASS = Dialog2FlowConfig | |
| BUILDER_CONFIGS = [] | |
| for dataset in DATASETS: | |
| BUILDER_CONFIGS.append( | |
| Dialog2FlowConfig( | |
| name=dataset, | |
| description="", | |
| citation=_CITATION, | |
| url="https://github.com/idiap/dialog2flow", | |
| )) | |
| DEFAULT_CONFIG_NAME = "dialog-acts+slots" | |
| def _info(self): | |
| if self.config.name in DATASETS_PRETRAIN: | |
| features = {"utterance": Value("string"), "label": Value("string")} | |
| else: | |
| features = {"dialog": [ | |
| { | |
| "speaker": Value("string"), | |
| "text": Value("string"), | |
| "domains": [ | |
| Value("string") | |
| ], | |
| "labels": { | |
| "dialog_acts": { | |
| "acts" : [Value("string")], | |
| "main_acts" : [Value("string")], | |
| "original_acts" : [Value("string")], | |
| }, | |
| "slots": [Value("string")], | |
| "intents": [Value("string")] | |
| } | |
| } | |
| ]} | |
| return DatasetInfo( | |
| description="", | |
| features=Features(features), | |
| homepage=self.config.url, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| if self.config.name in DATASETS_PRETRAIN: | |
| file_path = dl_manager.download({ | |
| "train": f"{self.config.name}.csv", # full | |
| "val": "val.csv", # few shot subset | |
| # "test": "test.csv", # TODO: use SpokenWOZ | |
| }) | |
| splits = [ | |
| SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "file_path": file_path["train"], | |
| "split": datasets.Split.TRAIN, | |
| }, | |
| ), | |
| SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "file_path": file_path["val"], | |
| "split": datasets.Split.VALIDATION, | |
| }, | |
| ), | |
| # SplitGenerator( # TODO: use SpokenWOZ | |
| # name=datasets.Split.TEST, | |
| # gen_kwargs={ | |
| # "file_path": file_path["test"], | |
| # "split": datasets.Split.TEST, | |
| # }, | |
| # ) | |
| ] | |
| else: | |
| splits = [] | |
| file_path = dl_manager.download({ | |
| "train": os.path.join(self.config.name, "data.json") | |
| }) | |
| split_names = DATASETS_DS[self.config.name] | |
| for split_name in split_names: | |
| splits.append( | |
| SplitGenerator( | |
| name=SPLIT2NAME[split_name], | |
| gen_kwargs={ | |
| "file_path": file_path["train"], | |
| "split": SPLIT2NAME[split_name], | |
| "split_name": split_name | |
| }, | |
| ) | |
| ) | |
| return splits | |
| def _load_json(self, file_path): | |
| with open(file_path, encoding="utf-8") as f: | |
| data = json.load(f) | |
| return data | |
| def _generate_examples(self, file_path, split, split_name=None): | |
| if split_name is not None: | |
| data = self._load_json(file_path) | |
| data = [(dial_id, dial) for dial_id, dial in data["dialogs"].items() if split_name in dial_id] | |
| logger.info(f"generating {len(data)} examples from = {split}") | |
| for dial_id, dial in data: | |
| yield dial_id, {"dialog": dial} | |
| else: | |
| with open(file_path, newline='') as csvfile: | |
| csvreader = csv.reader(csvfile) | |
| for ix, row in enumerate(csvreader): | |
| yield ix, {"utterance": row[0], "label": row[1]} | |