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import os |
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from unicodedata import name |
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import datasets |
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_CITATION = """\ |
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@article{bordes2016learning, |
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title={Learning end-to-end goal-oriented dialog}, |
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author={Bordes, Antoine and Boureau, Y-Lan and Weston, Jason}, |
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journal={arXiv preprint arXiv:1605.07683}, |
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year={2016} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This section presents the set of 6 tasks for testing end-to-end dialog systems in the restaurant domain described in the paper: |
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Antoine Bordes, Y-Lan Boureau, Jason Weston, Learning End-to-End Goal-Oriented Dialog, arxiv:1605.07683. |
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Each task tests a unique aspect of dialog. Tasks are designed to complement the set of 20 bAbI tasks for story understanding of the previous section. |
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For each task, there are 1000 dialogs for training, 1000 for development and 1000 for testing. For tasks 1-5, we also include a second test set (with suffix -OOV.txt) that contains dialogs including entities not present in training and development sets. |
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""" |
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_HOMEPAGE = "https://research.facebook.com/downloads/babi/" |
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_LICENSE = "data/LICENSE.txt" |
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DIALOG_BABI_ARCHIVE_URL = "https://www.dropbox.com/s/20rgyj8rryvos9l/dialog-bAbI-tasks-1_6.zip?dl=1" |
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class DialogBabiDataset(datasets.GeneratorBasedBuilder): |
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"""Facebook's dataset 'dialog-babi' for end-to-end dialogue learning.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="task1-API-calls", version=VERSION, description="Issuing API calls"), |
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datasets.BuilderConfig(name="task2-API-refine", version=VERSION, description="Updating API calls"), |
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datasets.BuilderConfig(name="task3-options", version=VERSION, description="Displaying options"), |
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datasets.BuilderConfig(name="task4-phone-address", version=VERSION, description="Providing extra information"), |
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datasets.BuilderConfig(name="task5-full-dialogs", version=VERSION, description="Conducting full dialogs"), |
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datasets.BuilderConfig(name="task6-dstc2", version=VERSION, description="2nd Dialog State Tracking Challenge (Henderson et al., 2014a)"), |
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] |
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DEFAULT_CONFIG_NAME = "task1-API-calls" |
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def _info(self): |
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if self.config.name == "task1-API-calls": |
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features = datasets.Features( |
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{ |
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"user_turns": datasets.Sequence(datasets.Value("string")), |
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"system_turns": datasets.Sequence(datasets.Value("string")) |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"user_turns": datasets.Sequence(datasets.Value("string")), |
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"system_turns": datasets.Sequence(datasets.Value("string")) |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(DIALOG_BABI_ARCHIVE_URL) |
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task_data_dir = os.path.join(data_dir, "dialog-bAbI-tasks-1_6", self.config.name) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(task_data_dir, f"dialog-babi-{self.config.name}-trn.txt"), |
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"split": f"{self.config.name}-train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(task_data_dir, f"dialog-babi-{self.config.name}-dev.txt"), |
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"split": f"{self.config.name}-dev", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(task_data_dir, f"dialog-babi-{self.config.name}-tst.txt"), |
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"split": f"{self.config.name}-test" |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("TESTOOV"), |
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gen_kwargs={ |
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"filepath": os.path.join(task_data_dir, f"dialog-babi-{self.config.name}-tst-OOV.txt"), |
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"split": f"{self.config.name}-test-OOV" |
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}, |
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) |
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] |
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def _generate_examples(self, filepath, split): |
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with open(filepath, encoding="utf-8") as f: |
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dialogue_rows = [] |
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dialogue_id = 1 |
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for row in f: |
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turn = row.strip() |
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if not turn: |
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yield self._format_dialogue(dialogue_id,split, dialogue_rows) |
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dialogue_rows.clear() |
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dialogue_id += 1 |
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else: |
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dialogue_rows.append(turn) |
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if dialogue_rows: |
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yield self._format_dialogue(dialogue_id, split, dialogue_rows) |
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def _format_dialogue(self, dialogue_id, split, dialogue_rows): |
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user_turns = [] |
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system_turns = [] |
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for turn in dialogue_rows: |
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rest_turn, sys_turn = turn.split("\t") |
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_, user_turn = rest_turn.split(" ", 1) |
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user_turns.append(user_turn) |
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system_turns.append(sys_turn) |
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example_key = f"{split}-{dialogue_id}" |
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return example_key, { |
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"user_turns": user_turns, |
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"system_turns": system_turns |
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} |
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