Datasets:

GEM
/

schema_guided_dialog

	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",
	]


	def process_sgd(example):
	prompt = example["prompt"]
	inp = f'Prompt: "{prompt}", '
	for da in example["dialog_acts"]:
	act = _SGD_ACTS[da["act"]].lower()
	slot = da["slot"]
	values = " or ".join(da["values"])
	inp += f"Response Type: {act}"
	if slot:
	inp += f", Type of Slot: {slot}"
	if values:
	inp += f", Values: {values}"
	inp += ", "
	inp += f'Agent: {example["service"]}'

	return inp


	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")],
	"linearized_input": 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_}"
	exple["linearized_input"] = process_sgd(exple)
	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."

	exple = {
	"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": [example["target"]],
	}
	exple["linearized_input"] = process_sgd(exple)

	yield id_, exple