Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-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. | |
"""NLU Evaluation Data.""" | |
from __future__ import absolute_import, division, print_function | |
import csv | |
import re | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """\ | |
@InProceedings{XLiu.etal:IWSDS2019, | |
author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser}, | |
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents}, | |
booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS)}, | |
month = {April}, | |
year = {2019}, | |
address = {Ortigia, Siracusa (SR), Italy}, | |
publisher = {Springer}, | |
pages = {xxx--xxx}, | |
url = {http://www.xx.xx/xx/} | |
} | |
""" | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
Raw part of NLU Evaluation Data. It contains 25 715 non-empty examples (original dataset has 25716 examples) from 68 unique intents belonging to 18 scenarios. | |
""" | |
_HOMEPAGE = "https://github.com/xliuhw/NLU-Evaluation-Data" | |
_LICENSE = "Creative Commons Attribution 4.0 International License (CC BY 4.0)" | |
_URL = "https://raw.githubusercontent.com/xliuhw/NLU-Evaluation-Data/master/AnnotatedData/NLU-Data-Home-Domain-Annotated-All.csv" | |
ANNOTATION_PATTERN = re.compile(r"\[(.+?)\s+\:+\s(.+?)\]") | |
def remove_annotations(text): | |
"""Remove named entity annotations from text example. | |
Examples are defined based on `answer_annotation` column since it has the least number | |
of Nans. However, this column contains patterns of annotation of the form: | |
[named_entity : part_of_text] | |
e.g. [time : five am], [date : this week] | |
We identity them with regex rule and replace all occurrences with just part_of_text. | |
""" | |
return ANNOTATION_PATTERN.sub(r"\2", text) | |
def define_intent_name(scenario, intent): | |
"""Intent name is defined as concatenation of `scenario` and `intent` | |
values. | |
See Also: | |
https://github.com/xliuhw/NLU-Evaluation-Data/issues/5 | |
""" | |
return f"{scenario}_{intent}" | |
class NLUEvaluationData(datasets.GeneratorBasedBuilder): | |
"""Raw part of NLU Evaluation Data.""" | |
VERSION = datasets.Version("1.1.0") | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"scenario": datasets.Value("string"), | |
"label": datasets.features.ClassLabel( | |
names=[ | |
"alarm_query", | |
"alarm_remove", | |
"alarm_set", | |
"audio_volume_down", | |
"audio_volume_mute", | |
"audio_volume_other", | |
"audio_volume_up", | |
"calendar_query", | |
"calendar_remove", | |
"calendar_set", | |
"cooking_query", | |
"cooking_recipe", | |
"datetime_convert", | |
"datetime_query", | |
"email_addcontact", | |
"email_query", | |
"email_querycontact", | |
"email_sendemail", | |
"general_affirm", | |
"general_commandstop", | |
"general_confirm", | |
"general_dontcare", | |
"general_explain", | |
"general_greet", | |
"general_joke", | |
"general_negate", | |
"general_praise", | |
"general_quirky", | |
"general_repeat", | |
"iot_cleaning", | |
"iot_coffee", | |
"iot_hue_lightchange", | |
"iot_hue_lightdim", | |
"iot_hue_lightoff", | |
"iot_hue_lighton", | |
"iot_hue_lightup", | |
"iot_wemo_off", | |
"iot_wemo_on", | |
"lists_createoradd", | |
"lists_query", | |
"lists_remove", | |
"music_dislikeness", | |
"music_likeness", | |
"music_query", | |
"music_settings", | |
"news_query", | |
"play_audiobook", | |
"play_game", | |
"play_music", | |
"play_podcasts", | |
"play_radio", | |
"qa_currency", | |
"qa_definition", | |
"qa_factoid", | |
"qa_maths", | |
"qa_stock", | |
"recommendation_events", | |
"recommendation_locations", | |
"recommendation_movies", | |
"social_post", | |
"social_query", | |
"takeaway_order", | |
"takeaway_query", | |
"transport_query", | |
"transport_taxi", | |
"transport_ticket", | |
"transport_traffic", | |
"weather_query", | |
] | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
train_path = dl_manager.download_and_extract(_URL) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), | |
] | |
def _generate_examples(self, filepath): | |
"""Yields examples as (key, example) tuples.""" | |
with open(filepath, encoding="utf-8") as f: | |
csv_reader = csv.reader(f, quotechar='"', delimiter=";", quoting=csv.QUOTE_ALL, skipinitialspace=True) | |
# call next to skip header | |
next(csv_reader) | |
for id_, row in enumerate(csv_reader): | |
( | |
userid, | |
answerid, | |
scenario, | |
intent, | |
status, | |
answer_annotation, | |
notes, | |
suggested_entities, | |
answer_normalised, | |
answer, | |
question, | |
) = row | |
# examples with empty answer are removed as part of the dataset | |
if answer_annotation == "null": | |
continue | |
yield id_, { | |
"text": remove_annotations(answer_annotation), | |
"scenario": scenario, | |
"label": define_intent_name(scenario, intent), | |
} | |