File size: 7,062 Bytes
e5d1748 d91d186 8199c76 d91d186 e5d1748 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
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"]],
}
|