task_categories:text-generation task_categories:fill-mask task_categories:text-classification task_ids:dialogue-modeling task_ids:language-modeling task_ids:masked-language-modeling annotations_creators:expert-generated language_creators:expert-generated multilinguality:multilingual size_categories:10K<n<100K source_datasets:original language:de language:en language:es language:fr language:it license:cc-by-sa-4.0 dialogue-act-classification region:us
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.
@unpublished{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}
}
---
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
    num_bytes: 149855
    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-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## 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](https://creativecommons.org/licenses/by-sa/4.0/).

### 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](https://github.com/eusip) and [@PierreColombo](https://github.com/PierreColombo) for adding this dataset.