
# 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.
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
{"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", "config_names": ["dihana", "ilisten", "loria", "maptask", "vm2"], "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}]}
TAGS
#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-German #language-English #language-Spanish #language-French #language-Italian #license-cc-by-sa-4.0 #dialogue-act-classification #region-us 

Dataset Card for MIAM
=====================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ 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


### Languages


English, French, German, Italian, Spanish.


Dataset Structure
-----------------


### Data Instances


#### Dihana Corpus


For the 'dihana' configuration one example from the dataset is:


#### iLISTEN Corpus


For the 'ilisten' configuration one example from the dataset is:


#### LORIA Corpus


For the 'loria' configuration one example from the dataset is:


#### HCRC MapTask Corpus


For the 'maptask' configuration one example from the dataset is:


#### VERBMOBIL


For the 'vm2' configuration one example from the dataset is:


### 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 Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


Anonymous.


### Licensing Information


This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License.


### Contributions


Thanks to @eusip and @PierreColombo for adding this dataset.



### 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 ### Languages


English, French, German, Italian, Spanish.


Dataset Structure
----------------- ### Data Instances #### Dihana Corpus


For the 'dihana' configuration one example from the dataset is: #### iLISTEN Corpus


For the 'ilisten' configuration one example from the dataset is: #### LORIA Corpus


For the 'loria' configuration one example from the dataset is: #### HCRC MapTask Corpus


For the 'maptask' configuration one example from the dataset is: #### VERBMOBIL


For the 'vm2' configuration one example from the dataset is: ### 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 Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


Anonymous. ### Licensing Information


This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. ### Contributions


Thanks to @eusip and @PierreColombo for adding this dataset.
TAGS
#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-German #language-English #language-Spanish #language-French #language-Italian #license-cc-by-sa-4.0 #dialogue-act-classification #region-us 
 ### 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 ### Languages


English, French, German, Italian, Spanish.


Dataset Structure
----------------- ### Data Instances #### Dihana Corpus


For the 'dihana' configuration one example from the dataset is: #### iLISTEN Corpus


For the 'ilisten' configuration one example from the dataset is: #### LORIA Corpus


For the 'loria' configuration one example from the dataset is: #### HCRC MapTask Corpus


For the 'maptask' configuration one example from the dataset is: #### VERBMOBIL


For the 'vm2' configuration one example from the dataset is: ### 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 Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


Anonymous. ### Licensing Information


This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. ### Contributions


Thanks to @eusip and @PierreColombo for adding this dataset.
passage: TAGS
#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-German #language-English #language-Spanish #language-French #language-Italian #license-cc-by-sa-4.0 #dialogue-act-classification #region-us 
### 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### Languages


English, French, German, Italian, Spanish.


Dataset Structure
-----------------### Data Instances#### Dihana Corpus


For the 'dihana' configuration one example from the dataset is:#### iLISTEN Corpus


For the 'ilisten' configuration one example from the dataset is:#### LORIA Corpus


For the 'loria' configuration one example from the dataset is:#### HCRC MapTask Corpus


For the 'maptask' configuration one example from the dataset is:#### VERBMOBIL


For the 'vm2' configuration one example from the dataset is: