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# 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.
# Lint as: python3
"""The Multilingual dIalogAct benchMark."""
import textwrap
import pandas as pd
import datasets
_MIAM_CITATION = """\
@unpublished{
anonymous2021cross-lingual,
title={Cross-Lingual Pretraining Methods for Spoken Dialog},
author={Anonymous},
journal={OpenReview Preprint},
year={2021},
url{https://openreview.net/forum?id=c1oDhu_hagR},
note={anonymous preprint under review}
}
"""
_MIAM_DESCRIPTION = """\
Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and
analyzing natural language understanding systems specifically designed for spoken language. Datasets
are in English, French, German, Italian and Spanish. They cover a variety of domains including
spontaneous speech, scripted scenarios, and joint task completion. Some datasets additionally include
emotion and/or sentimant labels.
"""
_URL = "https://raw.githubusercontent.com/eusip/MIAM/main"
DIHANA_DA_DESCRIPTION = {
"Afirmacion": "Feedback_positive",
"Apertura": "Opening",
"Cierre": "Closing",
"Confirmacion": "Acknowledge",
"Espera": "Hold",
"Indefinida": "Undefined",
"Negacion": "Feedback_negative",
"No_entendido": "Request_clarify",
"Nueva_consulta": "New_request",
"Pregunta": "Request",
"Respuesta": "Reply",
}
class MiamConfig(datasets.BuilderConfig):
"""BuilderConfig for MIAM."""
def __init__(
self,
text_features,
label_column,
data_url,
citation,
url,
label_classes=None,
**kwargs,
):
"""BuilderConfig for MIAM.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
label_column: `string`, name of the column in the csv/txt file corresponding
to the label
data_url: `string`, url to download the csv/text file from
citation: `string`, citation for the data set
url: `string`, url for information about the data set
label_classes: `list[string]`, the list of classes if the label is
categorical. If not provided, then the label will be of type
`datasets.Value('float32')`.
**kwargs: keyword arguments forwarded to super.
"""
super(MiamConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.text_features = text_features
self.label_column = label_column
self.label_classes = label_classes
self.data_url = data_url
self.citation = citation
self.url = url
class Miam(datasets.GeneratorBasedBuilder):
"""The Multilingual dIalogAct benchMark."""
BUILDER_CONFIGS = [
MiamConfig(
name="dihana",
description=textwrap.dedent(
"""\
The Dihana corpus primarily consists of spontaneous speech. The corpus is annotated
using three different levels of labels. The first level is dedicated to the generic
task-independent DA and the two additional are made with task-specific information. We
focus on the 11 first level tags."""
),
text_features={
"Speaker": "Speaker",
"Utterance": "Utterance",
"Dialogue_Act": "Dialogue_Act",
"Dialogue_ID": "Dialogue_ID",
"File_ID": "File_ID",
},
label_classes=list(DIHANA_DA_DESCRIPTION.keys()),
label_column="Dialogue_Act",
data_url={
"train": _URL + "/dihana/train.csv",
"dev": _URL + "/dihana/dev.csv",
"test": _URL + "/dihana/test.csv",
},
citation=textwrap.dedent(
"""\
@inproceedings{benedi2006design,
title={Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA},
author={Bened{\'i}, Jos{\'e}-Miguel and Lleida, Eduardo and Varona, Amparo and Castro, Mar{\'i}a-Jos{\'e} and Galiano, Isabel and Justo, Raquel and L{\'o}pez, I and Miguel, Antonio},
booktitle={Fifth International Conference on Language Resources and Evaluation (LREC)},
pages={1636--1639},
year={2006}
}
@inproceedings{post2013improved,
title={Improved speech-to-text translation with the Fisher and Callhome Spanish--English speech translation corpus},
author={Post, Matt and Kumar, Gaurav and Lopez, Adam and Karakos, Damianos and Callison-Burch, Chris and Khudanpur, Sanjeev},
booktitle={Proc. IWSLT},
year={2013}
}
@article{coria2005predicting,
title={Predicting obligation dialogue acts from prosodic and speaker infomation},
author={Coria, S and Pineda, L},
journal={Research on Computing Science (ISSN 1665-9899), Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Mexico City},
year={2005}
}"""
),
url="",
),
MiamConfig(
name="ilisten",
description=textwrap.dedent(
"""\
"itaLIan Speech acT labEliNg" (iLISTEN) is a corpus of annotated dialogue turns labeled
for speech acts."""
),
text_features={
"Speaker": "Speaker",
"Utterance": "Utterance",
"Dialogue_Act": "Dialogue_Act",
"Dialogue_ID": "Dialogue_ID",
},
label_classes=[
"AGREE",
"ANSWER",
"CLOSING",
"ENCOURAGE-SORRY",
"GENERIC-ANSWER",
"INFO-REQUEST",
"KIND-ATTITUDE_SMALL-TALK",
"OFFER-GIVE-INFO",
"OPENING",
"PERSUASION-SUGGEST",
"QUESTION",
"REJECT",
"SOLICITATION-REQ_CLARIFICATION",
"STATEMENT",
"TALK-ABOUT-SELF",
],
label_column="Dialogue_Act",
data_url={
"train": _URL + "/ilisten/train.csv",
"dev": _URL + "/ilisten/dev.csv",
"test": _URL + "/ilisten/test.csv",
},
citation=textwrap.dedent(
"""\
@article{basile2018overview,
title={Overview of the Evalita 2018itaLIan Speech acT labEliNg (iLISTEN) Task},
author={Basile, Pierpaolo and Novielli, Nicole},
journal={EVALITA Evaluation of NLP and Speech Tools for Italian},
volume={12},
pages={44},
year={2018}
}"""
),
url="",
),
MiamConfig(
name="loria",
description=textwrap.dedent(
"""\
The LORIA Nancy dialog corpus is derived from human-machine interactions in a serious
game setting."""
),
text_features={
"Speaker": "Speaker",
"Utterance": "Utterance",
"Dialogue_Act": "Dialogue_Act",
"Dialogue_ID": "Dialogue_ID",
"File_ID": "File_ID",
},
label_classes=[
"ack",
"ask",
"find_mold",
"find_plans",
"first_step",
"greet",
"help",
"inform",
"inform_engine",
"inform_job",
"inform_material_space",
"informer_conditioner",
"informer_decoration",
"informer_elcomps",
"informer_end_manufacturing",
"kindAtt",
"manufacturing_reqs",
"next_step",
"no",
"other",
"quality_control",
"quit",
"reqRep",
"security_policies",
"staff_enterprise",
"staff_job",
"studies_enterprise",
"studies_job",
"todo_failure",
"todo_irreparable",
"yes",
],
label_column="Dialogue_Act",
data_url={
"train": _URL + "/loria/train.csv",
"dev": _URL + "/loria/dev.csv",
"test": _URL + "/loria/test.csv",
},
citation=textwrap.dedent(
"""\
@inproceedings{barahona2012building,
title={Building and exploiting a corpus of dialog interactions between french speaking virtual and human agents},
author={Barahona, Lina Maria Rojas and Lorenzo, Alejandra and Gardent, Claire},
booktitle={The eighth international conference on Language Resources and Evaluation (LREC)},
pages={1428--1435},
year={2012}
}"""
),
url="",
),
MiamConfig(
name="maptask",
description=textwrap.dedent(
"""\
The HCRC MapTask Corpus was constructed through the verbal collaboration of participants
in order to construct a map route. This corpus is small (27k utterances). As there is
no standard train/dev/test split performance depends on the split."""
),
text_features={
"Speaker": "Speaker",
"Utterance": "Utterance",
"Dialogue_Act": "Dialogue_Act",
"Dialogue_ID": "Dialogue_ID",
"File_ID": "File_ID",
},
label_classes=[
"acknowledge",
"align",
"check",
"clarify",
"explain",
"instruct",
"query_w",
"query_yn",
"ready",
"reply_n",
"reply_w",
"reply_y",
],
label_column="Dialogue_Act",
data_url={
"train": _URL + "/maptask/train.csv",
"dev": _URL + "/maptask/dev.csv",
"test": _URL + "/maptask/test.csv",
},
citation=textwrap.dedent(
"""\
@inproceedings{thompson1993hcrc,
title={The HCRC map task corpus: natural dialogue for speech recognition},
author={Thompson, Henry S and Anderson, Anne H and Bard, Ellen Gurman and Doherty-Sneddon,
Gwyneth and Newlands, Alison and Sotillo, Cathy},
booktitle={HUMAN LANGUAGE TECHNOLOGY: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993},
year={1993}
}"""
),
url="http://groups.inf.ed.ac.uk/maptask/",
),
MiamConfig(
name="vm2",
description=textwrap.dedent(
"""\
The VERBMOBIL corpus consist of transcripts of multi-party meetings hand-annotated with
dialog acts. It is the second biggest dataset with around 110k utterances."""
),
text_features={
"Utterance": "Utterance",
"Dialogue_Act": "Dialogue_Act",
"Speaker": "Speaker",
"Dialogue_ID": "Dialogue_ID",
},
label_classes=[
"ACCEPT",
"BACKCHANNEL",
"BYE",
"CLARIFY",
"CLOSE",
"COMMIT",
"CONFIRM",
"DEFER",
"DELIBERATE",
"DEVIATE_SCENARIO",
"EXCLUDE",
"EXPLAINED_REJECT",
"FEEDBACK",
"FEEDBACK_NEGATIVE",
"FEEDBACK_POSITIVE",
"GIVE_REASON",
"GREET",
"INFORM",
"INIT",
"INTRODUCE",
"NOT_CLASSIFIABLE",
"OFFER",
"POLITENESS_FORMULA",
"REJECT",
"REQUEST",
"REQUEST_CLARIFY",
"REQUEST_COMMENT",
"REQUEST_COMMIT",
"REQUEST_SUGGEST",
"SUGGEST",
"THANK",
],
label_column="Dialogue_Act",
data_url={
"train": _URL + "/vm2/train.csv",
"dev": _URL + "/vm2/dev.csv",
"test": _URL + "/vm2/test.csv",
},
citation=textwrap.dedent(
"""\
@book{kay1992verbmobil,
title={Verbmobil: A translation system for face-to-face dialog},
author={Kay, Martin},
year={1992},
publisher={University of Chicago Press}
}"""
),
url="",
),
]
def _info(self):
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
if self.config.label_classes:
features["Label"] = datasets.features.ClassLabel(names=self.config.label_classes)
features["Idx"] = datasets.Value("int32")
return datasets.DatasetInfo(
description=_MIAM_DESCRIPTION,
features=datasets.Features(features),
homepage=self.config.url,
citation=self.config.citation + "\n" + _MIAM_CITATION,
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download(self.config.data_url)
splits = []
splits.append(
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": data_files["train"],
"split": "train",
},
)
)
splits.append(
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": data_files["dev"],
"split": "dev",
},
)
)
splits.append(
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": data_files["test"],
"split": "test",
},
)
)
return splits
def _generate_examples(self, data_file, split):
df = pd.read_csv(data_file, delimiter=",", header=0, quotechar='"', dtype=str)[
self.config.text_features.keys()
]
rows = df.to_dict(orient="records")
for n, row in enumerate(rows):
example = row
example["Idx"] = n
if self.config.label_column in example:
label = example[self.config.label_column]
example["Label"] = label
yield example["Idx"], example