Datasets:

qanastek
/

MASSIVE

	---
	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]",
	"tokens": [
	"réveille-moi",
	"à",
	"neuf",
	"heures",
	"du",
	"matin",
	"le",
	"vendredi"
	],
	"ner_tags": [0, 0, 71, 6, 6, 6, 0, 14],
	"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:

	Apache License
	Version 2.0, January 2004
	http://www.apache.org/licenses/

	TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

	1. Definitions.

	"License" shall mean the terms and conditions for use, reproduction,
	and distribution as defined by Sections 1 through 9 of this document.

	"Licensor" shall mean the copyright owner or entity authorized by
	the copyright owner that is granting the License.

	"Legal Entity" shall mean the union of the acting entity and all
	other entities that control, are controlled by, or are under common
	control with that entity. For the purposes of this definition,
	"control" means (i) the power, direct or indirect, to cause the
	direction or management of such entity, whether by contract or
	otherwise, or (ii) ownership of fifty percent (50%) or more of the
	outstanding shares, or (iii) beneficial ownership of such entity.

	"You" (or "Your") shall mean an individual or Legal Entity
	exercising permissions granted by this License.

	"Source" form shall mean the preferred form for making modifications,
	including but not limited to software source code, documentation
	source, and configuration files.

	"Object" form shall mean any form resulting from mechanical
	transformation or translation of a Source form, including but
	not limited to compiled object code, generated documentation,
	and conversions to other media types.

	"Work" shall mean the work of authorship, whether in Source or
	Object form, made available under the License, as indicated by a
	copyright notice that is included in or attached to the work
	(an example is provided in the Appendix below).

	"Derivative Works" shall mean any work, whether in Source or Object
	form, that is based on (or derived from) the Work and for which the
	editorial revisions, annotations, elaborations, or other modifications
	represent, as a whole, an original work of authorship. For the purposes
	of this License, Derivative Works shall not include works that remain
	separable from, or merely link (or bind by name) to the interfaces of,
	the Work and Derivative Works thereof.

	"Contribution" shall mean any work of authorship, including
	the original version of the Work and any modifications or additions
	to that Work or Derivative Works thereof, that is intentionally
	submitted to Licensor for inclusion in the Work by the copyright owner
	or by an individual or Legal Entity authorized to submit on behalf of
	the copyright owner. For the purposes of this definition, "submitted"
	means any form of electronic, verbal, or written communication sent
	to the Licensor or its representatives, including but not limited to
	communication on electronic mailing lists, source code control systems,
	and issue tracking systems that are managed by, or on behalf of, the
	Licensor for the purpose of discussing and improving the Work, but
	excluding communication that is conspicuously marked or otherwise
	designated in writing by the copyright owner as "Not a Contribution."

	"Contributor" shall mean Licensor and any individual or Legal Entity
	on behalf of whom a Contribution has been received by Licensor and
	subsequently incorporated within the Work.

	2. Grant of Copyright License. Subject to the terms and conditions of
	this License, each Contributor hereby grants to You a perpetual,
	worldwide, non-exclusive, no-charge, royalty-free, irrevocable
	copyright license to reproduce, prepare Derivative Works of,
	publicly display, publicly perform, sublicense, and distribute the
	Work and such Derivative Works in Source or Object form.

	3. Grant of Patent License. Subject to the terms and conditions of
	this License, each Contributor hereby grants to You a perpetual,
	worldwide, non-exclusive, no-charge, royalty-free, irrevocable
	(except as stated in this section) patent license to make, have made,
	use, offer to sell, sell, import, and otherwise transfer the Work,
	where such license applies only to those patent claims licensable
	by such Contributor that are necessarily infringed by their
	Contribution(s) alone or by combination of their Contribution(s)
	with the Work to which such Contribution(s) was submitted. If You
	institute patent litigation against any entity (including a
	cross-claim or counterclaim in a lawsuit) alleging that the Work
	or a Contribution incorporated within the Work constitutes direct
	or contributory patent infringement, then any patent licenses
	granted to You under this License for that Work shall terminate
	as of the date such litigation is filed.

	4. Redistribution. You may reproduce and distribute copies of the
	Work or Derivative Works thereof in any medium, with or without
	modifications, and in Source or Object form, provided that You
	meet the following conditions:

	(a) You must give any other recipients of the Work or
	Derivative Works a copy of this License; and

	(b) You must cause any modified files to carry prominent notices
	stating that You changed the files; and

	(c) You must retain, in the Source form of any Derivative Works
	that You distribute, all copyright, patent, trademark, and
	attribution notices from the Source form of the Work,
	excluding those notices that do not pertain to any part of
	the Derivative Works; and

	(d) If the Work includes a "NOTICE" text file as part of its
	distribution, then any Derivative Works that You distribute must
	include a readable copy of the attribution notices contained
	within such NOTICE file, excluding those notices that do not
	pertain to any part of the Derivative Works, in at least one
	of the following places: within a NOTICE text file distributed
	as part of the Derivative Works; within the Source form or
	documentation, if provided along with the Derivative Works; or,
	within a display generated by the Derivative Works, if and
	wherever such third-party notices normally appear. The contents
	of the NOTICE file are for informational purposes only and
	do not modify the License. You may add Your own attribution
	notices within Derivative Works that You distribute, alongside
	or as an addendum to the NOTICE text from the Work, provided
	that such additional attribution notices cannot be construed
	as modifying the License.

	You may add Your own copyright statement to Your modifications and
	may provide additional or different license terms and conditions
	for use, reproduction, or distribution of Your modifications, or
	for any such Derivative Works as a whole, provided Your use,
	reproduction, and distribution of the Work otherwise complies with
	the conditions stated in this License.

	5. Submission of Contributions. Unless You explicitly state otherwise,
	any Contribution intentionally submitted for inclusion in the Work
	by You to the Licensor shall be under the terms and conditions of
	this License, without any additional terms or conditions.
	Notwithstanding the above, nothing herein shall supersede or modify
	the terms of any separate license agreement you may have executed
	with Licensor regarding such Contributions.

	6. Trademarks. This License does not grant permission to use the trade
	names, trademarks, service marks, or product names of the Licensor,
	except as required for reasonable and customary use in describing the
	origin of the Work and reproducing the content of the NOTICE file.

	7. Disclaimer of Warranty. Unless required by applicable law or
	agreed to in writing, Licensor provides the Work (and each
	Contributor provides its Contributions) on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
	implied, including, without limitation, any warranties or conditions
	of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
	PARTICULAR PURPOSE. You are solely responsible for determining the
	appropriateness of using or redistributing the Work and assume any
	risks associated with Your exercise of permissions under this License.

	8. Limitation of Liability. In no event and under no legal theory,
	whether in tort (including negligence), contract, or otherwise,
	unless required by applicable law (such as deliberate and grossly
	negligent acts) or agreed to in writing, shall any Contributor be
	liable to You for damages, including any direct, indirect, special,
	incidental, or consequential damages of any character arising as a
	result of this License or out of the use or inability to use the
	Work (including but not limited to damages for loss of goodwill,
	work stoppage, computer failure or malfunction, or any and all
	other commercial damages or losses), even if such Contributor
	has been advised of the possibility of such damages.

	9. Accepting Warranty or Additional Liability. While redistributing
	the Work or Derivative Works thereof, You may choose to offer,
	and charge a fee for, acceptance of support, warranty, indemnity,
	or other liability obligations and/or rights consistent with this
	License. However, in accepting such obligations, You may act only
	on Your own behalf and on Your sole responsibility, not on behalf
	of any other Contributor, and only if You agree to indemnify,
	defend, and hold each Contributor harmless for any liability
	incurred by, or claims asserted against, such Contributor by reason
	of your accepting any such warranty or additional liability.

	END OF TERMS AND CONDITIONS

	APPENDIX: How to apply the Apache License to your work.

	To apply the Apache License to your work, attach the following
	boilerplate notice, with the fields enclosed by brackets "[]"
	replaced with your own identifying information. (Don't include
	the brackets!) The text should be enclosed in the appropriate
	comment syntax for the file format. We also recommend that a
	file or class name and description of purpose be included on the
	same "printed page" as the copyright notice for easier
	identification within third-party archives.

	Copyright [yyyy] [name of copyright owner]

	Licensed under the Apache License, Version 2.0 (the "License");
	you may not use this file except in compliance with the License.
	You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License.
	```

	### 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."
	}
	```