| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| """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| @unpublished{ |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 |
|
|
