Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""SGD: The Schema Guided Dialogue dataet""" | |
import json | |
import datasets | |
_CITATION = """\ | |
@inproceedings{aaai/RastogiZSGK20, | |
author = {Abhinav Rastogi and | |
Xiaoxue Zang and | |
Srinivas Sunkara and | |
Raghav Gupta and | |
Pranav Khaitan}, | |
title = {Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided | |
Dialogue Dataset}, | |
booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} | |
2020, The Thirty-Second Innovative Applications of Artificial Intelligence | |
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational | |
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA, | |
February 7-12, 2020}, | |
pages = {8689--8696}, | |
publisher = {{AAAI} Press}, | |
year = {2020}, | |
url = {https://aaai.org/ojs/index.php/AAAI/article/view/6394} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The Schema-Guided Dialogue dataset (SGD) was developed for the Dialogue State Tracking task of the Eights Dialogue Systems Technology Challenge (dstc8). | |
The SGD dataset consists of over 18k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. | |
These conversations involve interactions with services and APIs spanning 17 domains, ranging from banks and events to media, calendar, travel, and weather. | |
For most of these domains, the SGD dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, | |
which reflects common real-world scenarios. | |
""" | |
_HOMEPAGE = "https://github.com/google-research-datasets/dstc8-schema-guided-dialogue" | |
_LICENSE = "CC BY-SA 4.0" | |
_URL_LIST = [ | |
( | |
"train_schema.json", | |
"https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/raw/master/train/schema.json", | |
), | |
( | |
"dev_schema.json", | |
"https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/raw/master/dev/schema.json", | |
), | |
( | |
"test_schema.json", | |
"https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/raw/master/test/schema.json", | |
), | |
] | |
_URL_LIST += [ | |
( | |
f"train_dialogues_{i:03d}.json", | |
f"https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/raw/master/train/dialogues_{i:03d}.json", | |
) | |
for i in range(1, 128) | |
] | |
_URL_LIST += [ | |
( | |
f"dev_dialogues_{i:03d}.json", | |
f"https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/raw/master/dev/dialogues_{i:03d}.json", | |
) | |
for i in range(1, 21) | |
] | |
_URL_LIST += [ | |
( | |
f"test_dialogues_{i:03d}.json", | |
f"https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/raw/master/test/dialogues_{i:03d}.json", | |
) | |
for i in range(1, 35) | |
] | |
_URLs = dict(_URL_LIST) | |
_USER_ACTS = [ | |
"INFORM_INTENT", | |
"NEGATE_INTENT", | |
"AFFIRM_INTENT", | |
"INFORM", | |
"REQUEST", | |
"AFFIRM", | |
"NEGATE", | |
"SELECT", | |
"REQUEST_ALTS", | |
"THANK_YOU", | |
"GOODBYE", | |
] | |
_SYSTEM_ACTS = [ | |
"INFORM", | |
"REQUEST", | |
"CONFIRM", | |
"OFFER", | |
"NOTIFY_SUCCESS", | |
"NOTIFY_FAILURE", | |
"INFORM_COUNT", | |
"OFFER_INTENT", | |
"REQ_MORE", | |
"GOODBYE", | |
] | |
_ALL_ACTS = sorted(list(set(_USER_ACTS + _SYSTEM_ACTS))) | |
class SchemaGuidedDstc8(datasets.GeneratorBasedBuilder): | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name="dialogues", description="The dataset of annotated dialogues."), | |
datasets.BuilderConfig(name="schema", description="The schemas corresponding to the API calls."), | |
] | |
DEFAULT_CONFIG_NAME = "dialogues" | |
def _info(self): | |
if self.config.name == "schema": | |
features = datasets.Features( | |
{ | |
"service_name": datasets.Value("string"), | |
"description": datasets.Value("string"), | |
"slots": datasets.Sequence( | |
{ | |
"name": datasets.Value("string"), | |
"description": datasets.Value("string"), | |
"is_categorical": datasets.Value("bool"), | |
"possible_values": datasets.Sequence(datasets.Value("string")), | |
} | |
), | |
"intents": datasets.Sequence( | |
{ | |
"name": datasets.Value("string"), | |
"description": datasets.Value("string"), | |
"is_transactional": datasets.Value("bool"), | |
"required_slots": datasets.Sequence(datasets.Value("string")), | |
# optional_slots was originally a dictionary | |
"optional_slots": datasets.Sequence( | |
{ | |
"slot_name": datasets.Value("string"), | |
"slot_value": datasets.Value("string"), | |
} | |
), | |
"result_slots": datasets.Sequence(datasets.Value("string")), | |
}, | |
), | |
} | |
) | |
else: | |
features = datasets.Features( | |
{ | |
"dialogue_id": datasets.Value("string"), | |
"services": datasets.Sequence(datasets.Value("string")), | |
"turns": datasets.Sequence( | |
{ | |
"speaker": datasets.ClassLabel(names=["USER", "SYSTEM"]), | |
"utterance": datasets.Value("string"), | |
"frames": datasets.Sequence( | |
{ | |
"service": datasets.Value("string"), | |
"slots": datasets.Sequence( | |
{ | |
"slot": datasets.Value("string"), | |
"start": datasets.Value("int32"), | |
"exclusive_end": datasets.Value("int32"), | |
} | |
), | |
# optional | |
"state": { | |
"active_intent": datasets.Value("string"), | |
"requested_slots": datasets.Sequence(datasets.Value("string")), | |
# slot_values was originally a dictionary | |
"slot_values": datasets.Sequence( | |
{ | |
"slot_name": datasets.Value("string"), | |
"slot_value_list": datasets.Sequence(datasets.Value("string")), | |
} | |
), | |
}, | |
"actions": datasets.Sequence( | |
{ | |
"act": datasets.ClassLabel(names=_ALL_ACTS), | |
# optional | |
"slot": datasets.Value("string"), | |
# optional | |
"canonical_values": datasets.Sequence(datasets.Value("string")), | |
# optional | |
"values": datasets.Sequence(datasets.Value("string")), | |
} | |
), | |
# optional | |
"service_results": datasets.Sequence( | |
# Arrow doesn't like Sequences of Sequences for default values so we need a Sequence of Features of Sequences | |
{ | |
"service_results_list": datasets.Sequence( | |
# originally each list item was a dictionary (optional) | |
{ | |
"service_slot_name": datasets.Value("string"), | |
"service_canonical_value": datasets.Value("string"), | |
} | |
) | |
} | |
), | |
# optional | |
"service_call": { | |
"method": datasets.Value("string"), | |
# parameters was originally a dictionary | |
"parameters": datasets.Sequence( | |
{ | |
"parameter_slot_name": datasets.Value("string"), | |
"parameter_canonical_value": datasets.Value("string"), | |
} | |
), | |
}, | |
} | |
), | |
} | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, # Here we define them above because they are different between the two configurations | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_files = dl_manager.download_and_extract(_URLs) | |
return [ | |
datasets.SplitGenerator( | |
name=spl_enum, | |
gen_kwargs={ | |
"filepaths": data_files, | |
"split": spl, | |
}, | |
) | |
for spl, spl_enum in [ | |
("train", datasets.Split.TRAIN), | |
("dev", datasets.Split.VALIDATION), | |
("test", datasets.Split.TEST), | |
] | |
] | |
def _generate_examples(self, filepaths, split): | |
id_ = -1 | |
file_list = [fpath for fname, fpath in filepaths.items() if fname.startswith(f"{split}_{self.config.name}")] | |
for filepath in file_list: | |
examples = json.load(open(filepath, encoding="utf-8")) | |
for example in examples: | |
id_ += 1 | |
if self.config.name == "schema": | |
example["intents"] = example.get("intents", []) | |
for intent in example["intents"]: | |
optional_slots = intent.get("optional_slots", {}) | |
intent["optional_slots"] = { | |
"slot_name": list(optional_slots.keys()), | |
"slot_value": list(optional_slots.values()), | |
} | |
else: | |
for turn in example["turns"]: | |
for frame in turn["frames"]: | |
# add empty state if the key is missing from the dict | |
frame["state"] = frame.get( | |
"state", | |
{ | |
"active_intent": "", | |
"requested_slots": [], | |
"slot_values": {}, | |
}, | |
) | |
# linearize the optional slot_values dictionary | |
slot_values_dict = frame["state"].get("slot_values", {}) | |
frame["state"]["slot_values"] = { | |
"slot_name": list(slot_values_dict.keys()), | |
"slot_value_list": list(slot_values_dict.values()), | |
} | |
# add default values for optional fields in actions | |
for action in frame["actions"]: | |
action["slot"] = action.get("slot", "") | |
action["canonical_values"] = action.get("canonical_values", []) | |
action["values"] = action.get("values", []) | |
# add "service_results" field when necessary and linearize the dictionaries in the list otherwise | |
service_results = [] | |
for result in frame.get("service_results", []): | |
service_results += [ | |
{ | |
"service_slot_name": list(result.keys()), | |
"service_canonical_value": list(result.values()), | |
} | |
] | |
frame["service_results"] = { | |
"service_results_list": service_results, | |
} | |
# add "service_call" field when necessary and linearize the parameters dictionary otherwise | |
frame["service_call"] = frame.get( | |
"service_call", | |
{ | |
"method": "", | |
"parameters": {}, | |
}, | |
) | |
parameters_dict = frame["service_call"].get("parameters", {}) | |
frame["service_call"]["parameters"] = { | |
"parameter_slot_name": list(parameters_dict.keys()), | |
"parameter_canonical_value": list(parameters_dict.values()), | |
} | |
yield id_, example | |