Datasets:
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
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. | |
| # 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| @unpublished{ | |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 | |