README.md

metadata

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]
  • 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
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • No Warranty
    • 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

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

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

Dataset Structure

Data Instances (taken from Alexa Github)

{
  "id": "0",
  "locale": "de-DE",
  "partition": "test",
  "scenario": "alarm",
  "intent": "alarm_set",
  "utt": "weck mich diese woche um fünf uhr morgens auf",
  "annot_utt": "weck mich [date : diese woche] um [time : fünf uhr morgens] auf",
  "worker_id": "8",
  "slot_method": [
    {
      "slot": "time",
      "method": "translation"
    },
    {
      "slot": "date",
      "method": "translation"
    }
  ],
  "judgments": [
    {
      "worker_id": "32",
      "intent_score": 1,
      "slots_score": 0,
      "grammar_score": 4,
      "spelling_score": 2,
      "language_identification": "target"
    },
    {
      "worker_id": "8",
      "intent_score": 1,
      "slots_score": 1,
      "grammar_score": 4,
      "spelling_score": 2,
      "language_identification": "target"
    },
    {
      "worker_id": "28",
      "intent_score": 1,
      "slots_score": 1,
      "grammar_score": 4,
      "spelling_score": 2,
      "language_identification": "target"
    }
  ]
}

Data Fields (taken from Alexa Github)

id: maps to the original ID in the 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.

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.

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

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.

@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."
}