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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@inproceedings{rastogi2020towards, |
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title={Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset}, |
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author={Rastogi, Abhinav and Zang, Xiaoxue and Sunkara, Srinivas and Gupta, Raghav and Khaitan, Pranav}, |
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booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, |
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volume={34}, |
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number={05}, |
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pages={8689--8696}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The Schema-Guided Dialogue (SGD) dataset contains 18K multi-domain task-oriented |
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dialogues between a human and a virtual assistant, which covers 17 domains |
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ranging from banks and events to media, calendar, travel, and weather. The |
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language presents in the datset is only English. The SGD dataset provides a |
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challenging testbed for a number of tasks in task-oriented dialogue, including |
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language understanding, slot filling, dialogue state tracking and response |
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generation. For the creation of the SGD dataset, they developed a multi-domain |
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dialogue simulator that generates dialogue outlines over an arbitrary combination |
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of APIs, dialogue states and system actions. Then, they used a crowd-sourcing |
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procedure to paraphrase these outlines to natural language utterances. This novel |
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crowd-sourcing procedure preserves all annotations obtained from the simulator and |
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does not require any extra annotations after dialogue collection. |
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|
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""" |
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_URLs = { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_sgd_context.zip", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/schema_guided_dialog.zip", |
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} |
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_SGD_ACTS = [ |
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"AFFIRM", |
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"AFFIRM_INTENT", |
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"CONFIRM", |
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"GOODBYE", |
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"INFORM", |
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"INFORM_COUNT", |
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"INFORM_INTENT", |
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"NEGATE", |
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"NEGATE_INTENT", |
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"NOTIFY_FAILURE", |
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"NOTIFY_SUCCESS", |
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"OFFER", |
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"OFFER_INTENT", |
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"REQUEST", |
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"REQUEST_ALTS", |
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"REQ_MORE", |
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"SELECT", |
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"THANK_YOU", |
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] |
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def process_sgd(example): |
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prompt = example["prompt"] |
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inp = f'Prompt: "{prompt}", ' |
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for da in example["dialog_acts"]: |
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act = _SGD_ACTS[da["act"]].lower() |
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slot = da["slot"] |
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values = " or ".join(da["values"]) |
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inp += f"Response Type: {act}" |
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if slot: |
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inp += f", Type of Slot: {slot}" |
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if values: |
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inp += f", Values: {values}" |
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inp += ", " |
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inp += f'Agent: {example["service"]}' |
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return inp |
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class SchemaGuidedDialog(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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DEFAULT_CONFIG_NAME = "schema_guided_dialog" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
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"gem_parent_id": datasets.Value("string"), |
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"dialog_acts": [ |
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{ |
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"act": datasets.ClassLabel(names=_SGD_ACTS), |
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"slot": datasets.Value("string"), |
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"values": [datasets.Value("string")], |
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} |
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], |
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"context": [datasets.Value("string")], |
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"dialog_id": datasets.Value("string"), |
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"service": datasets.Value("string"), |
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"turn_id": datasets.Value("int32"), |
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"prompt": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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"linearized_input": 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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supervised_keys=None, |
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homepage="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_dir = dl_manager.download_and_extract(_URLs) |
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challenge_sets = [ |
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( |
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"challenge_train_sample", |
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"train_schema_guided_dialog_RandomSample500_reformatted.json", |
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), |
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( |
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"challenge_validation_sample", |
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"validation_schema_guided_dialog_RandomSample500_reformatted.json", |
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), |
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( |
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"challenge_test_backtranslation", |
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"test_schema_guided_dialog_BackTranslation500_reformatted.json", |
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), |
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( |
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"challenge_test_bfp02", |
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"test_schema_guided_dialog_ButterFingersPerturbation_p=0.02_500_reformatted.json", |
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), |
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( |
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"challenge_test_bfp05", |
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"test_schema_guided_dialog_ButterFingersPerturbation_p=0.05_500_reformatted.json", |
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), |
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( |
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"challenge_test_nopunc", |
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"test_schema_guided_dialog_WithoutPunctuation500_reformatted.json", |
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), |
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( |
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"challenge_test_scramble", |
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"test_schema_guided_dialog_ScrambleInputStructure500_reformatted.json", |
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), |
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] |
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return [ |
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datasets.SplitGenerator( |
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name=spl, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["data"], "gem_sgd.json"), |
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"split": spl, |
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}, |
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) |
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for spl in ["train", "validation", "test"] |
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] + [ |
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datasets.SplitGenerator( |
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name=challenge_split, |
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gen_kwargs={ |
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"filepath": os.path.join( |
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dl_dir["challenge_set"], "schema_guided_dialog", filename |
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), |
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"split": challenge_split, |
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}, |
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) |
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for challenge_split, filename in challenge_sets |
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] |
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def _generate_examples(self, filepath, split, filepaths=None, lang=None): |
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"""Yields examples.""" |
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if "challenge" in split: |
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exples = json.load(open(filepath, encoding="utf-8")) |
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if isinstance(exples, dict): |
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assert len(exples) == 1, "multiple entries found" |
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exples = list(exples.values())[0] |
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for id_, exple in enumerate(exples): |
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if len(exple) == 0: |
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continue |
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exple["gem_parent_id"] = exple["gem_id"] |
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exple["gem_id"] = f"schema_guided_dialog-{split}-{id_}" |
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exple["linearized_input"] = process_sgd(exple) |
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yield id_, exple |
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else: |
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examples = json.load(open(filepath, encoding="utf-8"))[split] |
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for id_, example in enumerate(examples): |
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|
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if not example["target"]: |
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example["target"] = "Thank you, goodbye." |
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exple = { |
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"gem_id": f"schema_guided_dialog-{split}-{id_}", |
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"gem_parent_id": f"schema_guided_dialog-{split}-{id_}", |
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"dialog_acts": [ |
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{ |
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"act": act_id, |
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"slot": slot, |
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"values": values, |
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} |
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for act_id, slot, values in example["da"] |
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], |
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"context": example["context"], |
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"dialog_id": example["dialog_id"], |
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"service": example["service"], |
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"turn_id": example["turn_ix"], |
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"prompt": example["prompt"], |
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"target": example["target"], |
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"references": [example["target"]], |
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} |
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exple["linearized_input"] = process_sgd(exple) |
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yield id_, exple |
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