Datasets:
Size:
10K<n<100K
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- de
- en
- es
- fr
- it
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialogue-modeling
- language-modeling
- masked-language-modeling
pretty_name: MIAM
tags:
- dialogue-act-classification
dataset_info:
- config_name: dihana
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: File_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': Afirmacion
'1': Apertura
'2': Cierre
'3': Confirmacion
'4': Espera
'5': Indefinida
'6': Negacion
'7': No_entendido
'8': Nueva_consulta
'9': Pregunta
'10': Respuesta
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 1946735
num_examples: 19063
- name: validation
num_bytes: 216498
num_examples: 2123
- name: test
num_bytes: 238446
num_examples: 2361
download_size: 1777267
dataset_size: 2401679
- config_name: ilisten
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': AGREE
'1': ANSWER
'2': CLOSING
'3': ENCOURAGE-SORRY
'4': GENERIC-ANSWER
'5': INFO-REQUEST
'6': KIND-ATTITUDE_SMALL-TALK
'7': OFFER-GIVE-INFO
'8': OPENING
'9': PERSUASION-SUGGEST
'10': QUESTION
'11': REJECT
'12': SOLICITATION-REQ_CLARIFICATION
'13': STATEMENT
'14': TALK-ABOUT-SELF
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 244336
num_examples: 1986
- name: validation
num_bytes: 33988
num_examples: 230
- name: test
num_bytes: 145376
num_examples: 971
download_size: 349993
dataset_size: 423700
- config_name: loria
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: File_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': ack
'1': ask
'2': find_mold
'3': find_plans
'4': first_step
'5': greet
'6': help
'7': inform
'8': inform_engine
'9': inform_job
'10': inform_material_space
'11': informer_conditioner
'12': informer_decoration
'13': informer_elcomps
'14': informer_end_manufacturing
'15': kindAtt
'16': manufacturing_reqs
'17': next_step
'18': 'no'
'19': other
'20': quality_control
'21': quit
'22': reqRep
'23': security_policies
'24': staff_enterprise
'25': staff_job
'26': studies_enterprise
'27': studies_job
'28': todo_failure
'29': todo_irreparable
'30': 'yes'
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 1208730
num_examples: 8465
- name: validation
num_bytes: 133829
num_examples: 942
- name: test
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num_examples: 1047
download_size: 1221132
dataset_size: 1492414
- config_name: maptask
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: File_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': acknowledge
'1': align
'2': check
'3': clarify
'4': explain
'5': instruct
'6': query_w
'7': query_yn
'8': ready
'9': reply_n
'10': reply_w
'11': reply_y
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 1910120
num_examples: 25382
- name: validation
num_bytes: 389879
num_examples: 5221
- name: test
num_bytes: 396947
num_examples: 5335
download_size: 1729021
dataset_size: 2696946
- config_name: vm2
features:
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Speaker
dtype: string
- name: Dialogue_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': ACCEPT
'1': BACKCHANNEL
'2': BYE
'3': CLARIFY
'4': CLOSE
'5': COMMIT
'6': CONFIRM
'7': DEFER
'8': DELIBERATE
'9': DEVIATE_SCENARIO
'10': EXCLUDE
'11': EXPLAINED_REJECT
'12': FEEDBACK
'13': FEEDBACK_NEGATIVE
'14': FEEDBACK_POSITIVE
'15': GIVE_REASON
'16': GREET
'17': INFORM
'18': INIT
'19': INTRODUCE
'20': NOT_CLASSIFIABLE
'21': OFFER
'22': POLITENESS_FORMULA
'23': REJECT
'24': REQUEST
'25': REQUEST_CLARIFY
'26': REQUEST_COMMENT
'27': REQUEST_COMMIT
'28': REQUEST_SUGGEST
'29': SUGGEST
'30': THANK
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 1869254
num_examples: 25060
- name: validation
num_bytes: 209390
num_examples: 2860
- name: test
num_bytes: 209032
num_examples: 2855
download_size: 1641453
dataset_size: 2287676
config_names:
- dihana
- ilisten
- loria
- maptask
- vm2
Dataset Card for MIAM
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: [N/A]
- Repository: [N/A]
- Paper: [N/A]
- Leaderboard: [N/A]
- Point of Contact: [N/A]
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.