README.md

---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
languages:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- kn-IN
- ko-KR
- lv-LV
- ml-IN
- mn-MN
- ms-MY
- my-MM
- nb-NO
- nl-NL
- pl-PL
- pt-PT
- ro-RO
- ru-RU
- sl-SL
- sq-AL
- sv-SE
- sw-KE
- ta-IN
- te-IN
- th-TH
- tl-PH
- tr-TR
- ur-PK
- vi-VN
- zh-CN
- zh-TW
licenses:
- apache-2.0
multilinguality:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- kn-IN
- ko-KR
- lv-LV
- ml-IN
- mn-MN
- ms-MY
- my-MM
- nb-NO
- nl-NL
- pl-PL
- pt-PT
- ro-RO
- ru-RU
- sl-SL
- sq-AL
- sv-SE
- sw-KE
- ta-IN
- te-IN
- th-TH
- tl-PH
- tr-TR
- ur-PK
- vi-VN
- zh-CN
- zh-TW
pretty_name: MASSIVE
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
- natural-language-understanding
---

# MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages

## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
  - [Table of Contents](#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)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Personal and Sensitive Information](#personal-and-sensitive-information)
  - [Considerations for Using the Data](#considerations-for-using-the-data)
    - [Other Known Limitations](#other-known-limitations)
  - [Additional Information](#additional-information)
    - [Dataset Curators](#dataset-curators)
    - [Licensing Information](#licensing-information)
    - [No Warranty](#no-warranty)
    - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** https://github.com/alexa/massive
- **Repository:** https://github.com/alexa/massive
- **Paper:** https://arxiv.org/abs/2204.08582
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview
- **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues)

### Dataset Summary

MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.

| Name | Lang  |    Utt/Lang    | Domains | Intents  |  Slots |
|:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:|
| MASSIVE                                                                         | 51      | 19,521         | 18      | 60       | 55     |
| SLURP (Bastianelli et al., 2020)                                                | 1       | 16,521         | 18      | 60       | 55     |
| NLU Evaluation Data (Liu et al., 2019)                                          | 1       | 25,716         | 18      | 54       | 56     |
| Airline Travel Information System (ATIS) (Price, 1990)                          | 1       | 5,871          | 1       | 26       | 129    |
| ATIS with Hindi and Turkish (Upadhyay et al., 2018)                             | 3       | 1,315-5,871    | 1       | 26       | 129    |
| MultiATIS++ (Xu et al., 2020)                                                   | 9       | 1,422-5,897    | 1       | 21-26    | 99-140 |
| Snips (Coucke et al., 2018)                                                     | 1       | 14,484         |  -      | 7        | 53     |
| Snips with French (Saade et al., 2019)                                          | 2       | 4,818          | 2       | 14-15    | 11-12  |
| Task Oriented Parsing (TOP) (Gupta et al., 2018)                                | 1       | 44,873         | 2       | 25       | 36     |
| Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021)            | 6       | 15,195-22,288  | 11      | 104-113  | 72-75  |
| Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019)         | 3       | 5,083-43,323   | 3       | 12       | 11     |
| Microsoft Dialog Challenge (Li et al., 2018)                                    | 1       | 38,276         | 3       | 11       | 29     |
| Fluent Speech Commands (FSC) (Lugosch et al., 2019)                             | 1       | 30,043         |  -      | 31       |  -     |
| Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1       | 16,258         | 4       |  -       | 94     |

### Supported Tasks and Leaderboards

The dataset can be used to train a model for `natural-language-understanding` (NLU) :

- `intent-classification`
- `multi-class-classification`
- `natural-language-understanding`

### Languages

The corpora consists of parallel sentences from 51 languages :

- `Afrikaans - South Africa (af-ZA)`
- `Amharic - Ethiopia (am-ET)`
- `Arabic - Saudi Arabia (ar-SA)`
- `Azeri - Azerbaijan (az-AZ)`
- `Bengali - Bangladesh (bn-BD)`
- `Chinese - China (zh-CN)`
- `Chinese - Taiwan (zh-TW)`
- `Danish - Denmark (da-DK)`
- `German - Germany (de-DE)`
- `Greek - Greece (el-GR)`
- `English - United States (en-US)`
- `Spanish - Spain (es-ES)`
- `Farsi - Iran (fa-IR)`
- `Finnish - Finland (fi-FI)`
- `French - France (fr-FR)`
- `Hebrew - Israel (he-IL)`
- `Hungarian - Hungary (hu-HU)`
- `Armenian - Armenia (hy-AM)`
- `Indonesian - Indonesia (id-ID)`
- `Icelandic - Iceland (is-IS)`
- `Italian - Italy (it-IT)`
- `Japanese - Japan (ja-JP)`
- `Javanese - Indonesia (jv-ID)`
- `Georgian - Georgia (ka-GE)`
- `Khmer - Cambodia (km-KH)`
- `Korean - Korea (ko-KR)`
- `Latvian - Latvia (lv-LV)`
- `Mongolian - Mongolia (mn-MN)`
- `Malay - Malaysia (ms-MY)`
- `Burmese - Myanmar (my-MM)`
- `Norwegian - Norway (nb-NO)`
- `Dutch - Netherlands (nl-NL)`
- `Polish - Poland (pl-PL)`
- `Portuguese - Portugal (pt-PT)`
- `Romanian - Romania (ro-RO)`
- `Russian - Russia (ru-RU)`
- `Slovanian - Slovania (sl-SL)`
- `Albanian - Albania (sq-AL)`
- `Swedish - Sweden (sv-SE)`
- `Swahili - Kenya (sw-KE)`
- `Hindi - India (hi-IN)`
- `Kannada - India (kn-IN)`
- `Malayalam - India (ml-IN)`
- `Tamil - India (ta-IN)`
- `Telugu - India (te-IN)`
- `Thai - Thailand (th-TH)`
- `Tagalog - Philippines (tl-PH)`
- `Turkish - Turkey (tr-TR)`
- `Urdu - Pakistan (ur-PK)`
- `Vietnamese - Vietnam (vi-VN)`
- `Welsh - United Kingdom (cy-GB)`

## Load the dataset with HuggingFace

```python
from datasets import load_dataset
dataset = load_dataset("qanastek/MASSIVE", "en-US", split='train')
print(dataset)
print(dataset[0])
```

## Dataset Structure

### Data Instances

```json
{
  "id": "1",
  "locale": "fr-FR",
  "partition": "train",
  "scenario": 16,
  "intent": 48,
  "utt": "réveille-moi à neuf heures du matin le vendredi",
  "annot_utt": "réveille-moi à [time : neuf heures du matin] le [date : vendredi]",
  "annot_utt_bio": {
    "word": [
      "réveille-moi",
      "à",
      "neuf",
      "heures",
      "du",
      "matin",
      "le",
      "vendredi"
    ],
    "tag": ["O", "O", "B-time", "I-time", "I-time", "I-time", "O", "B-date"]
  },
  "worker_id": "22",
  "slot_method": {
    "slot": ["time", "date"],
    "method": ["translation", "translation"]
  },
  "judgments": {
    "worker_id": ["11", "22", "0"],
    "intent_score": [2, 1, 1],
    "slots_score": [1, 1, 1],
    "grammar_score": [3, 4, 4],
    "spelling_score": [2, 2, 2],
    "language_identification": ["target", "target", "target"]
  }
}
```

### Data Fields (taken from Alexa Github)

`id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.

`locale`: is the language and country code accoring to ISO-639-1 and ISO-3166.

`partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp).

`scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance

`intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}`

`utt`: the raw utterance text without annotations

`annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]`

`worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales.

`slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification).

`judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.

```plain
intent_score : "Does the sentence match the intent?"
  0: No
  1: Yes
  2: It is a reasonable interpretation of the goal

slots_score : "Do all these terms match the categories in square brackets?"
  0: No
  1: Yes
  2: There are no words in square brackets (utterance without a slot)

grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?"
  0: Completely unnatural (nonsensical, cannot be understood at all)
  1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language)
  2: Some errors (the meaning can be understood but it doesn't sound natural in your language)
  3: Good enough (easily understood and sounds almost natural in your language)
  4: Perfect (sounds natural in your language)

spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error."
  0: There are more than 2 spelling errors
  1: There are 1-2 spelling errors
  2: All words are spelled correctly

language_identification : "The following sentence contains words in the following languages (check all that apply)"
  1: target
  2: english
  3: other
  4: target & english
  5: target & other
  6: english & other
  7: target & english & other
```

### Data Splits

|Language|Train|Dev|Test|
|:---:|:---:|:---:|:---:|
|af-ZA|11514|2033|2974|
|am-ET|11514|2033|2974|
|ar-SA|11514|2033|2974|
|az-AZ|11514|2033|2974|
|bn-BD|11514|2033|2974|
|cy-GB|11514|2033|2974|
|da-DK|11514|2033|2974|
|de-DE|11514|2033|2974|
|el-GR|11514|2033|2974|
|en-US|11514|2033|2974|
|es-ES|11514|2033|2974|
|fa-IR|11514|2033|2974|
|fi-FI|11514|2033|2974|
|fr-FR|11514|2033|2974|
|he-IL|11514|2033|2974|
|hi-IN|11514|2033|2974|
|hu-HU|11514|2033|2974|
|hy-AM|11514|2033|2974|
|id-ID|11514|2033|2974|
|is-IS|11514|2033|2974|
|it-IT|11514|2033|2974|
|ja-JP|11514|2033|2974|
|jv-ID|11514|2033|2974|
|ka-GE|11514|2033|2974|
|km-KH|11514|2033|2974|
|kn-IN|11514|2033|2974|
|ko-KR|11514|2033|2974|
|lv-LV|11514|2033|2974|
|ml-IN|11514|2033|2974|
|mn-MN|11514|2033|2974|
|ms-MY|11514|2033|2974|
|my-MM|11514|2033|2974|
|nb-NO|11514|2033|2974|
|nl-NL|11514|2033|2974|
|pl-PL|11514|2033|2974|
|pt-PT|11514|2033|2974|
|ro-RO|11514|2033|2974|
|ru-RU|11514|2033|2974|
|sl-SL|11514|2033|2974|
|sq-AL|11514|2033|2974|
|sv-SE|11514|2033|2974|
|sw-KE|11514|2033|2974|
|ta-IN|11514|2033|2974|
|te-IN|11514|2033|2974|
|th-TH|11514|2033|2974|
|tl-PH|11514|2033|2974|
|tr-TR|11514|2033|2974|
|ur-PK|11514|2033|2974|
|vi-VN|11514|2033|2974|
|zh-CN|11514|2033|2974|
|zh-TW|11514|2033|2974|

## Dataset Creation

### Source Data

#### Who are the source language producers?

The corpus has been produced and uploaded by Amazon Alexa.

### Personal and Sensitive Information

The corpora is free of personal or sensitive information.

## Additional Information

### Dataset Curators

__MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan.

__SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.

__Hugging Face__: Labrak Yanis (Not affiliated with the original corpus)

### Licensing Information

```plain
Copyright Amazon.com Inc. or its affiliates.

Copyright and license details for the data and modified code can be found in NOTICE.md.

License for massive repo and code, Apache 2.0:

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### Citation Information

Please cite the following paper when using this dataset.

```latex
@misc{fitzgerald2022massive,
      title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, 
      author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
      year={2022},
      eprint={2204.08582},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{bastianelli-etal-2020-slurp,
    title = "{SLURP}: A Spoken Language Understanding Resource Package",
    author = "Bastianelli, Emanuele  and
      Vanzo, Andrea  and
      Swietojanski, Pawel  and
      Rieser, Verena",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.588",
    doi = "10.18653/v1/2020.emnlp-main.588",
    pages = "7252--7262",
    abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."
}
```