Datasets:
Tasks:
Conversational
Languages:
English
Multilinguality:
unknown
Size Categories:
unknown
Language Creators:
unknown
Annotations Creators:
crowd-sourced
Source Datasets:
original
ArXiv:
License:
cc-by-sa-4.0
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 | |