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Dataset Card for MIAM
Dataset Summary
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. All datasets contain dialogue act labels.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English, French, German, Italian, Spanish.
Dataset Structure
Data Instances
Dihana Corpus
For the dihana
configuration one example from the dataset is:
{
'Speaker': 'U',
'Utterance': 'Hola , quería obtener el horario para ir a Valencia',
'Dialogue_Act': 9, # 'Pregunta' ('Request')
'Dialogue_ID': '0',
'File_ID': 'B209_BA5c3',
}
iLISTEN Corpus
For the ilisten
configuration one example from the dataset is:
{
'Speaker': 'T_11_U11',
'Utterance': 'ok, grazie per le informazioni',
'Dialogue_Act': 6, # 'KIND-ATTITUDE_SMALL-TALK'
'Dialogue_ID': '0',
}
LORIA Corpus
For the loria
configuration one example from the dataset is:
{
'Speaker': 'Samir',
'Utterance': 'Merci de votre visite, bonne chance, et à la prochaine !',
'Dialogue_Act': 21, # 'quit'
'Dialogue_ID': '5',
'File_ID': 'Dial_20111128_113927',
}
HCRC MapTask Corpus
For the maptask
configuration one example from the dataset is:
{
'Speaker': 'f',
'Utterance': 'is it underneath the rope bridge or to the left',
'Dialogue_Act': 6, # 'query_w'
'Dialogue_ID': '0',
'File_ID': 'q4ec1',
}
VERBMOBIL
For the vm2
configuration one example from the dataset is:
{
'Utterance': 'ja was sind viereinhalb Stunden Bahngerüttel gegen siebzig Minuten Turbulenzen im Flugzeug',
'Utterance': 'Utterance',
'Dialogue_Act': 'Dialogue_Act', # 'INFORM'
'Speaker': 'A',
'Dialogue_ID': '66',
}
Data Fields
For the dihana
configuration, the different fields are:
Speaker
: identifier of the speaker as a string.Utterance
: Utterance as a string.Dialogue_Act
: Dialog act label of the utterance. It can be one of 'Afirmacion' (0) [Feedback_positive], 'Apertura' (1) [Opening], 'Cierre' (2) [Closing], 'Confirmacion' (3) [Acknowledge], 'Espera' (4) [Hold], 'Indefinida' (5) [Undefined], 'Negacion' (6) [Feedback_negative], 'No_entendido' (7) [Request_clarify], 'Nueva_consulta' (8) [New_request], 'Pregunta' (9) [Request] or 'Respuesta' (10) [Reply].Dialogue_ID
: identifier of the dialogue as a string.File_ID
: identifier of the source file as a string.
For the ilisten
configuration, the different fields are:
Speaker
: identifier of the speaker as a string.Utterance
: Utterance as a string.Dialogue_Act
: Dialog act label of the utterance. It can be one of 'AGREE' (0), 'ANSWER' (1), 'CLOSING' (2), 'ENCOURAGE-SORRY' (3), 'GENERIC-ANSWER' (4), 'INFO-REQUEST' (5), 'KIND-ATTITUDE_SMALL-TALK' (6), 'OFFER-GIVE-INFO' (7), 'OPENING' (8), 'PERSUASION-SUGGEST' (9), 'QUESTION' (10), 'REJECT' (11), 'SOLICITATION-REQ_CLARIFICATION' (12), 'STATEMENT' (13) or 'TALK-ABOUT-SELF' (14).Dialogue_ID
: identifier of the dialogue as a string.
For the loria
configuration, the different fields are:
Speaker
: identifier of the speaker as a string.Utterance
: Utterance as a string.Dialogue_Act
: Dialog act label of the utterance. It can be one of 'ack' (0), 'ask' (1), 'find_mold' (2), 'find_plans' (3), 'first_step' (4), 'greet' (5), 'help' (6), 'inform' (7), 'inform_engine' (8), 'inform_job' (9), 'inform_material_space' (10), 'informer_conditioner' (11), 'informer_decoration' (12), 'informer_elcomps' (13), 'informer_end_manufacturing' (14), 'kindAtt' (15), 'manufacturing_reqs' (16), 'next_step' (17), 'no' (18), 'other' (19), 'quality_control' (20), 'quit' (21), 'reqRep' (22), 'security_policies' (23), 'staff_enterprise' (24), 'staff_job' (25), 'studies_enterprise' (26), 'studies_job' (27), 'todo_failure' (28), 'todo_irreparable' (29), 'yes' (30)Dialogue_ID
: identifier of the dialogue as a string.File_ID
: identifier of the source file as a string.
For the maptask
configuration, the different fields are:
Speaker
: identifier of the speaker as a string.Utterance
: Utterance as a string.Dialogue_Act
: Dialog act label of the utterance. It can be one of 'acknowledge' (0), 'align' (1), 'check' (2), 'clarify' (3), 'explain' (4), 'instruct' (5), 'query_w' (6), 'query_yn' (7), 'ready' (8), 'reply_n' (9), 'reply_w' (10) or 'reply_y' (11).Dialogue_ID
: identifier of the dialogue as a string.File_ID
: identifier of the source file as a string.
For the vm2
configuration, the different fields are:
Utterance
: Utterance as a string.Dialogue_Act
: Dialogue act label of the utterance. It can be one of 'ACCEPT' (0), 'BACKCHANNEL' (1), 'BYE' (2), 'CLARIFY' (3), 'CLOSE' (4), 'COMMIT' (5), 'CONFIRM' (6), 'DEFER' (7), 'DELIBERATE' (8), 'DEVIATE_SCENARIO' (9), 'EXCLUDE' (10), 'EXPLAINED_REJECT' (11), 'FEEDBACK' (12), 'FEEDBACK_NEGATIVE' (13), 'FEEDBACK_POSITIVE' (14), 'GIVE_REASON' (15), 'GREET' (16), 'INFORM' (17), 'INIT' (18), 'INTRODUCE' (19), 'NOT_CLASSIFIABLE' (20), 'OFFER' (21), 'POLITENESS_FORMULA' (22), 'REJECT' (23), 'REQUEST' (24), 'REQUEST_CLARIFY' (25), 'REQUEST_COMMENT' (26), 'REQUEST_COMMIT' (27), 'REQUEST_SUGGEST' (28), 'SUGGEST' (29), 'THANK' (30).Speaker
: Speaker as a string.Dialogue_ID
: identifier of the dialogue as a string.
Data Splits
Dataset name | Train | Valid | Test |
---|---|---|---|
dihana | 19063 | 2123 | 2361 |
ilisten | 1986 | 230 | 971 |
loria | 8465 | 942 | 1047 |
maptask | 25382 | 5221 | 5335 |
vm2 | 25060 | 2860 | 2855 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Anonymous.
Licensing Information
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License.
Citation Information
@inproceedings{colombo-etal-2021-code,
title = "Code-switched inspired losses for spoken dialog representations",
author = "Colombo, Pierre and
Chapuis, Emile and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.656",
doi = "10.18653/v1/2021.emnlp-main.656",
pages = "8320--8337",
abstract = "Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (\textit{i.e} multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings.",
}
Contributions
Thanks to @eusip and @PierreColombo for adding this dataset.
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