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