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- .gitattributes +0 -37
- CITATION.cff +0 -50
- LICENSE +0 -397
- README.md +0 -864
- af-ZA/massive-test.parquet +3 -0
- af-ZA/massive-train.parquet +3 -0
- af-ZA/massive-validation.parquet +3 -0
- all/massive-test.parquet +3 -0
- all/massive-train.parquet +3 -0
- all/massive-validation.parquet +3 -0
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- fa-IR/massive-test.parquet +3 -0
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- fi-FI/massive-test.parquet +3 -0
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CITATION.cff
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cff-version: 1.0.0
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message: "MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"
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authors:
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given-names: "Jack"
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given-names: "Christopher"
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given-names: "Charith"
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given-names: "Scott"
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given-names: "Kay"
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given-names: "Ana"
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given-names: "Aaron"
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given-names: "Liam"
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given-names: "Vishesh"
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given-names: "Richa"
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- family-names: "Ranganath"
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given-names: "Swetha"
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- family-names: "Crist"
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given-names: "Laurie"
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- family-names: "Britan"
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given-names: "Misha"
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given-names: "Wouter"
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given-names: "Gokhan"
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- family-names: "Natarajan"
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given-names: "Prem"
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title: "MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"
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version: 1.0.0
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date-released: 2022-04-18
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url: "https://arxiv.org/abs/2204.08582"
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LICENSE
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|
README.md
DELETED
@@ -1,864 +0,0 @@
|
|
1 |
-
---
|
2 |
-
annotations_creators:
|
3 |
-
- expert-generated
|
4 |
-
language_creators:
|
5 |
-
- found
|
6 |
-
license:
|
7 |
-
- cc-by-4.0
|
8 |
-
multilinguality:
|
9 |
-
- af-ZA
|
10 |
-
- am-ET
|
11 |
-
- ar-SA
|
12 |
-
- az-AZ
|
13 |
-
- bn-BD
|
14 |
-
- ca-ES
|
15 |
-
- cy-GB
|
16 |
-
- da-DK
|
17 |
-
- de-DE
|
18 |
-
- el-GR
|
19 |
-
- en-US
|
20 |
-
- es-ES
|
21 |
-
- fa-IR
|
22 |
-
- fi-FI
|
23 |
-
- fr-FR
|
24 |
-
- he-IL
|
25 |
-
- hi-IN
|
26 |
-
- hu-HU
|
27 |
-
- hy-AM
|
28 |
-
- id-ID
|
29 |
-
- is-IS
|
30 |
-
- it-IT
|
31 |
-
- ja-JP
|
32 |
-
- jv-ID
|
33 |
-
- ka-GE
|
34 |
-
- km-KH
|
35 |
-
- kn-IN
|
36 |
-
- ko-KR
|
37 |
-
- lv-LV
|
38 |
-
- ml-IN
|
39 |
-
- mn-MN
|
40 |
-
- ms-MY
|
41 |
-
- my-MM
|
42 |
-
- nb-NO
|
43 |
-
- nl-NL
|
44 |
-
- pl-PL
|
45 |
-
- pt-PT
|
46 |
-
- ro-RO
|
47 |
-
- ru-RU
|
48 |
-
- sl-SL
|
49 |
-
- sq-AL
|
50 |
-
- sv-SE
|
51 |
-
- sw-KE
|
52 |
-
- ta-IN
|
53 |
-
- te-IN
|
54 |
-
- th-TH
|
55 |
-
- tl-PH
|
56 |
-
- tr-TR
|
57 |
-
- ur-PK
|
58 |
-
- vi-VN
|
59 |
-
- zh-CN
|
60 |
-
- zh-TW
|
61 |
-
size_categories:
|
62 |
-
- 100K<n<1M
|
63 |
-
source_datasets:
|
64 |
-
- original
|
65 |
-
task_categories:
|
66 |
-
- text-classification
|
67 |
-
task_ids:
|
68 |
-
- intent-classification
|
69 |
-
- multi-class-classification
|
70 |
-
paperswithcode_id: massive
|
71 |
-
pretty_name: MASSIVE
|
72 |
-
language_bcp47:
|
73 |
-
- af-ZA
|
74 |
-
- am-ET
|
75 |
-
- ar-SA
|
76 |
-
- az-AZ
|
77 |
-
- bn-BD
|
78 |
-
- ca-ES
|
79 |
-
- cy-GB
|
80 |
-
- da-DK
|
81 |
-
- de-DE
|
82 |
-
- el-GR
|
83 |
-
- en-US
|
84 |
-
- es-ES
|
85 |
-
- fa-IR
|
86 |
-
- fi-FI
|
87 |
-
- fr-FR
|
88 |
-
- he-IL
|
89 |
-
- hi-IN
|
90 |
-
- hu-HU
|
91 |
-
- hy-AM
|
92 |
-
- id-ID
|
93 |
-
- is-IS
|
94 |
-
- it-IT
|
95 |
-
- ja-JP
|
96 |
-
- jv-ID
|
97 |
-
- ka-GE
|
98 |
-
- km-KH
|
99 |
-
- kn-IN
|
100 |
-
- ko-KR
|
101 |
-
- lv-LV
|
102 |
-
- ml-IN
|
103 |
-
- mn-MN
|
104 |
-
- ms-MY
|
105 |
-
- my-MM
|
106 |
-
- nb-NO
|
107 |
-
- nl-NL
|
108 |
-
- pl-PL
|
109 |
-
- pt-PT
|
110 |
-
- ro-RO
|
111 |
-
- ru-RU
|
112 |
-
- sl-SL
|
113 |
-
- sq-AL
|
114 |
-
- sv-SE
|
115 |
-
- sw-KE
|
116 |
-
- ta-IN
|
117 |
-
- te-IN
|
118 |
-
- th-TH
|
119 |
-
- tl-PH
|
120 |
-
- tr-TR
|
121 |
-
- ur-PK
|
122 |
-
- vi-VN
|
123 |
-
- zh-CN
|
124 |
-
- zh-TW
|
125 |
-
tags:
|
126 |
-
- natural-language-understanding
|
127 |
-
---
|
128 |
-
|
129 |
-
# MASSIVE 1.1: A 1M-Example Multilingual Natural Language Understanding Dataset with 52 Typologically-Diverse Languages
|
130 |
-
|
131 |
-
## Table of Contents
|
132 |
-
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
|
133 |
-
- [Table of Contents](#table-of-contents)
|
134 |
-
- [Dataset Description](#dataset-description)
|
135 |
-
- [Dataset Summary](#dataset-summary)
|
136 |
-
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
137 |
-
- [Languages](#languages)
|
138 |
-
- [Dataset Structure](#dataset-structure)
|
139 |
-
- [Data Instances](#data-instances)
|
140 |
-
- [Data Fields](#data-fields)
|
141 |
-
- [Data Splits](#data-splits)
|
142 |
-
- [Dataset Creation](#dataset-creation)
|
143 |
-
- [Curation Rationale](#curation-rationale)
|
144 |
-
- [Source Data](#source-data)
|
145 |
-
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
|
146 |
-
- [Who are the source language producers?](#who-are-the-source-language-producers)
|
147 |
-
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
148 |
-
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
149 |
-
- [Other Known Limitations](#other-known-limitations)
|
150 |
-
- [Additional Information](#additional-information)
|
151 |
-
- [Dataset Curators](#dataset-curators)
|
152 |
-
- [Licensing Information](#licensing-information)
|
153 |
-
- [No Warranty](#no-warranty)
|
154 |
-
- [Citation Information](#citation-information)
|
155 |
-
|
156 |
-
## Dataset Description
|
157 |
-
|
158 |
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- **Homepage:** https://github.com/alexa/massive
|
159 |
-
- **Repository:** https://github.com/alexa/massive
|
160 |
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- **Paper:** https://arxiv.org/abs/2204.08582
|
161 |
-
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview
|
162 |
-
- **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues)
|
163 |
-
|
164 |
-
### Dataset Summary
|
165 |
-
|
166 |
-
MASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 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.
|
167 |
-
|
168 |
-
| Name | Lang | Utt/Lang | Domains | Intents | Slots |
|
169 |
-
|:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:|
|
170 |
-
| MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 |
|
171 |
-
| SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 |
|
172 |
-
| NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 |
|
173 |
-
| Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 |
|
174 |
-
| ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 |
|
175 |
-
| MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 |
|
176 |
-
| Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 |
|
177 |
-
| Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 |
|
178 |
-
| Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 |
|
179 |
-
| Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 |
|
180 |
-
| Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 |
|
181 |
-
| Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 |
|
182 |
-
| Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - |
|
183 |
-
| Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 |
|
184 |
-
|
185 |
-
### Supported Tasks and Leaderboards
|
186 |
-
|
187 |
-
The dataset can be used to train a model for `natural-language-understanding` (NLU) :
|
188 |
-
|
189 |
-
- `intent-classification`
|
190 |
-
- `multi-class-classification`
|
191 |
-
- `natural-language-understanding`
|
192 |
-
|
193 |
-
### Languages
|
194 |
-
|
195 |
-
The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages :
|
196 |
-
|
197 |
-
- `Afrikaans - South Africa (af-ZA)`
|
198 |
-
- `Amharic - Ethiopia (am-ET)`
|
199 |
-
- `Arabic - Saudi Arabia (ar-SA)`
|
200 |
-
- `Azeri - Azerbaijan (az-AZ)`
|
201 |
-
- `Bengali - Bangladesh (bn-BD)`
|
202 |
-
- `Catalan - Spain (ca-ES)`
|
203 |
-
- `Chinese - China (zh-CN)`
|
204 |
-
- `Chinese - Taiwan (zh-TW)`
|
205 |
-
- `Danish - Denmark (da-DK)`
|
206 |
-
- `German - Germany (de-DE)`
|
207 |
-
- `Greek - Greece (el-GR)`
|
208 |
-
- `English - United States (en-US)`
|
209 |
-
- `Spanish - Spain (es-ES)`
|
210 |
-
- `Farsi - Iran (fa-IR)`
|
211 |
-
- `Finnish - Finland (fi-FI)`
|
212 |
-
- `French - France (fr-FR)`
|
213 |
-
- `Hebrew - Israel (he-IL)`
|
214 |
-
- `Hungarian - Hungary (hu-HU)`
|
215 |
-
- `Armenian - Armenia (hy-AM)`
|
216 |
-
- `Indonesian - Indonesia (id-ID)`
|
217 |
-
- `Icelandic - Iceland (is-IS)`
|
218 |
-
- `Italian - Italy (it-IT)`
|
219 |
-
- `Japanese - Japan (ja-JP)`
|
220 |
-
- `Javanese - Indonesia (jv-ID)`
|
221 |
-
- `Georgian - Georgia (ka-GE)`
|
222 |
-
- `Khmer - Cambodia (km-KH)`
|
223 |
-
- `Korean - Korea (ko-KR)`
|
224 |
-
- `Latvian - Latvia (lv-LV)`
|
225 |
-
- `Mongolian - Mongolia (mn-MN)`
|
226 |
-
- `Malay - Malaysia (ms-MY)`
|
227 |
-
- `Burmese - Myanmar (my-MM)`
|
228 |
-
- `Norwegian - Norway (nb-NO)`
|
229 |
-
- `Dutch - Netherlands (nl-NL)`
|
230 |
-
- `Polish - Poland (pl-PL)`
|
231 |
-
- `Portuguese - Portugal (pt-PT)`
|
232 |
-
- `Romanian - Romania (ro-RO)`
|
233 |
-
- `Russian - Russia (ru-RU)`
|
234 |
-
- `Slovanian - Slovania (sl-SL)`
|
235 |
-
- `Albanian - Albania (sq-AL)`
|
236 |
-
- `Swedish - Sweden (sv-SE)`
|
237 |
-
- `Swahili - Kenya (sw-KE)`
|
238 |
-
- `Hindi - India (hi-IN)`
|
239 |
-
- `Kannada - India (kn-IN)`
|
240 |
-
- `Malayalam - India (ml-IN)`
|
241 |
-
- `Tamil - India (ta-IN)`
|
242 |
-
- `Telugu - India (te-IN)`
|
243 |
-
- `Thai - Thailand (th-TH)`
|
244 |
-
- `Tagalog - Philippines (tl-PH)`
|
245 |
-
- `Turkish - Turkey (tr-TR)`
|
246 |
-
- `Urdu - Pakistan (ur-PK)`
|
247 |
-
- `Vietnamese - Vietnam (vi-VN)`
|
248 |
-
- `Welsh - United Kingdom (cy-GB)`
|
249 |
-
|
250 |
-
## Load the dataset with HuggingFace
|
251 |
-
|
252 |
-
```python
|
253 |
-
from datasets import load_dataset
|
254 |
-
dataset = load_dataset("AmazonScience/massive", "en-US", split='train')
|
255 |
-
print(dataset[0])
|
256 |
-
```
|
257 |
-
|
258 |
-
## Dataset Structure
|
259 |
-
|
260 |
-
### Data Instances
|
261 |
-
|
262 |
-
```json
|
263 |
-
{
|
264 |
-
"id": "0",
|
265 |
-
"locale": "fr-FR",
|
266 |
-
"partition": "test",
|
267 |
-
"scenario": "alarm",
|
268 |
-
"intent": "alarm_set",
|
269 |
-
"utt": "réveille-moi à cinq heures du matin cette semaine",
|
270 |
-
"annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]",
|
271 |
-
"worker_id": "22",
|
272 |
-
"slot_method": [
|
273 |
-
{ "slot": "time", "method": "translation" },
|
274 |
-
{ "slot": "date", "method": "translation" }
|
275 |
-
],
|
276 |
-
"judgments": [
|
277 |
-
{
|
278 |
-
"worker_id": "22",
|
279 |
-
"intent_score": 1,
|
280 |
-
"slots_score": 1,
|
281 |
-
"grammar_score": 4,
|
282 |
-
"spelling_score": 2,
|
283 |
-
"language_identification": "target"
|
284 |
-
},
|
285 |
-
{
|
286 |
-
"worker_id": "8",
|
287 |
-
"intent_score": 1,
|
288 |
-
"slots_score": 1,
|
289 |
-
"grammar_score": 4,
|
290 |
-
"spelling_score": 2,
|
291 |
-
"language_identification": "target"
|
292 |
-
},
|
293 |
-
{
|
294 |
-
"worker_id": "0",
|
295 |
-
"intent_score": 1,
|
296 |
-
"slots_score": 1,
|
297 |
-
"grammar_score": 4,
|
298 |
-
"spelling_score": 2,
|
299 |
-
"language_identification": "target"
|
300 |
-
}
|
301 |
-
]
|
302 |
-
}
|
303 |
-
```
|
304 |
-
|
305 |
-
### Data Fields
|
306 |
-
|
307 |
-
`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.
|
308 |
-
|
309 |
-
`locale`: is the language and country code accoring to ISO-639-1 and ISO-3166.
|
310 |
-
|
311 |
-
`partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp).
|
312 |
-
|
313 |
-
`scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance
|
314 |
-
|
315 |
-
`intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}`
|
316 |
-
|
317 |
-
`utt`: the raw utterance text without annotations
|
318 |
-
|
319 |
-
`annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]`
|
320 |
-
|
321 |
-
`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.
|
322 |
-
|
323 |
-
`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).
|
324 |
-
|
325 |
-
`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.
|
326 |
-
|
327 |
-
```plain
|
328 |
-
intent_score : "Does the sentence match the intent?"
|
329 |
-
0: No
|
330 |
-
1: Yes
|
331 |
-
2: It is a reasonable interpretation of the goal
|
332 |
-
|
333 |
-
slots_score : "Do all these terms match the categories in square brackets?"
|
334 |
-
0: No
|
335 |
-
1: Yes
|
336 |
-
2: There are no words in square brackets (utterance without a slot)
|
337 |
-
|
338 |
-
grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?"
|
339 |
-
0: Completely unnatural (nonsensical, cannot be understood at all)
|
340 |
-
1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language)
|
341 |
-
2: Some errors (the meaning can be understood but it doesn't sound natural in your language)
|
342 |
-
3: Good enough (easily understood and sounds almost natural in your language)
|
343 |
-
4: Perfect (sounds natural in your language)
|
344 |
-
|
345 |
-
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."
|
346 |
-
0: There are more than 2 spelling errors
|
347 |
-
1: There are 1-2 spelling errors
|
348 |
-
2: All words are spelled correctly
|
349 |
-
|
350 |
-
language_identification : "The following sentence contains words in the following languages (check all that apply)"
|
351 |
-
1: target
|
352 |
-
2: english
|
353 |
-
3: other
|
354 |
-
4: target & english
|
355 |
-
5: target & other
|
356 |
-
6: english & other
|
357 |
-
7: target & english & other
|
358 |
-
```
|
359 |
-
|
360 |
-
### Data Splits
|
361 |
-
|
362 |
-
|Language|Train|Dev|Test|
|
363 |
-
|:---:|:---:|:---:|:---:|
|
364 |
-
|af-ZA|11514|2033|2974|
|
365 |
-
|am-ET|11514|2033|2974|
|
366 |
-
|ar-SA|11514|2033|2974|
|
367 |
-
|az-AZ|11514|2033|2974|
|
368 |
-
|bn-BD|11514|2033|2974|
|
369 |
-
|ca-ES|11514|2033|2974|
|
370 |
-
|cy-GB|11514|2033|2974|
|
371 |
-
|da-DK|11514|2033|2974|
|
372 |
-
|de-DE|11514|2033|2974|
|
373 |
-
|el-GR|11514|2033|2974|
|
374 |
-
|en-US|11514|2033|2974|
|
375 |
-
|es-ES|11514|2033|2974|
|
376 |
-
|fa-IR|11514|2033|2974|
|
377 |
-
|fi-FI|11514|2033|2974|
|
378 |
-
|fr-FR|11514|2033|2974|
|
379 |
-
|he-IL|11514|2033|2974|
|
380 |
-
|hi-IN|11514|2033|2974|
|
381 |
-
|hu-HU|11514|2033|2974|
|
382 |
-
|hy-AM|11514|2033|2974|
|
383 |
-
|id-ID|11514|2033|2974|
|
384 |
-
|is-IS|11514|2033|2974|
|
385 |
-
|it-IT|11514|2033|2974|
|
386 |
-
|ja-JP|11514|2033|2974|
|
387 |
-
|jv-ID|11514|2033|2974|
|
388 |
-
|ka-GE|11514|2033|2974|
|
389 |
-
|km-KH|11514|2033|2974|
|
390 |
-
|kn-IN|11514|2033|2974|
|
391 |
-
|ko-KR|11514|2033|2974|
|
392 |
-
|lv-LV|11514|2033|2974|
|
393 |
-
|ml-IN|11514|2033|2974|
|
394 |
-
|mn-MN|11514|2033|2974|
|
395 |
-
|ms-MY|11514|2033|2974|
|
396 |
-
|my-MM|11514|2033|2974|
|
397 |
-
|nb-NO|11514|2033|2974|
|
398 |
-
|nl-NL|11514|2033|2974|
|
399 |
-
|pl-PL|11514|2033|2974|
|
400 |
-
|pt-PT|11514|2033|2974|
|
401 |
-
|ro-RO|11514|2033|2974|
|
402 |
-
|ru-RU|11514|2033|2974|
|
403 |
-
|sl-SL|11514|2033|2974|
|
404 |
-
|sq-AL|11514|2033|2974|
|
405 |
-
|sv-SE|11514|2033|2974|
|
406 |
-
|sw-KE|11514|2033|2974|
|
407 |
-
|ta-IN|11514|2033|2974|
|
408 |
-
|te-IN|11514|2033|2974|
|
409 |
-
|th-TH|11514|2033|2974|
|
410 |
-
|tl-PH|11514|2033|2974|
|
411 |
-
|tr-TR|11514|2033|2974|
|
412 |
-
|ur-PK|11514|2033|2974|
|
413 |
-
|vi-VN|11514|2033|2974|
|
414 |
-
|zh-CN|11514|2033|2974|
|
415 |
-
|zh-TW|11514|2033|2974|
|
416 |
-
|
417 |
-
|
418 |
-
### Personal and Sensitive Information
|
419 |
-
|
420 |
-
The corpora is free of personal or sensitive information.
|
421 |
-
|
422 |
-
## Additional Information
|
423 |
-
|
424 |
-
### Dataset Curators
|
425 |
-
|
426 |
-
__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.
|
427 |
-
|
428 |
-
__SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.
|
429 |
-
|
430 |
-
__Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus)
|
431 |
-
|
432 |
-
### Licensing Information
|
433 |
-
|
434 |
-
```plain
|
435 |
-
Copyright Amazon.com Inc. or its affiliates.
|
436 |
-
|
437 |
-
Attribution 4.0 International
|
438 |
-
|
439 |
-
=======================================================================
|
440 |
-
|
441 |
-
Creative Commons Corporation ("Creative Commons") is not a law firm and
|
442 |
-
does not provide legal services or legal advice. Distribution of
|
443 |
-
Creative Commons public licenses does not create a lawyer-client or
|
444 |
-
other relationship. Creative Commons makes its licenses and related
|
445 |
-
information available on an "as-is" basis. Creative Commons gives no
|
446 |
-
warranties regarding its licenses, any material licensed under their
|
447 |
-
terms and conditions, or any related information. Creative Commons
|
448 |
-
disclaims all liability for damages resulting from their use to the
|
449 |
-
fullest extent possible.
|
450 |
-
|
451 |
-
Using Creative Commons Public Licenses
|
452 |
-
|
453 |
-
Creative Commons public licenses provide a standard set of terms and
|
454 |
-
conditions that creators and other rights holders may use to share
|
455 |
-
original works of authorship and other material subject to copyright
|
456 |
-
and certain other rights specified in the public license below. The
|
457 |
-
following considerations are for informational purposes only, are not
|
458 |
-
exhaustive, and do not form part of our licenses.
|
459 |
-
|
460 |
-
Considerations for licensors: Our public licenses are
|
461 |
-
intended for use by those authorized to give the public
|
462 |
-
permission to use material in ways otherwise restricted by
|
463 |
-
copyright and certain other rights. Our licenses are
|
464 |
-
irrevocable. Licensors should read and understand the terms
|
465 |
-
and conditions of the license they choose before applying it.
|
466 |
-
Licensors should also secure all rights necessary before
|
467 |
-
applying our licenses so that the public can reuse the
|
468 |
-
material as expected. Licensors should clearly mark any
|
469 |
-
material not subject to the license. This includes other CC-
|
470 |
-
licensed material, or material used under an exception or
|
471 |
-
limitation to copyright. More considerations for licensors:
|
472 |
-
wiki.creativecommons.org/Considerations_for_licensors
|
473 |
-
|
474 |
-
Considerations for the public: By using one of our public
|
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-
licenses, a licensor grants the public permission to use the
|
476 |
-
licensed material under specified terms and conditions. If
|
477 |
-
the licensor's permission is not necessary for any reason--for
|
478 |
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example, because of any applicable exception or limitation to
|
479 |
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copyright--then that use is not regulated by the license. Our
|
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-
licenses grant only permissions under copyright and certain
|
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other rights that a licensor has authority to grant. Use of
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the licensed material may still be restricted for other
|
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reasons, including because others have copyright or other
|
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rights in the material. A licensor may make special requests,
|
485 |
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such as asking that all changes be marked or described.
|
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Although not required by our licenses, you are encouraged to
|
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respect those requests where reasonable. More considerations
|
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for the public:
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489 |
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wiki.creativecommons.org/Considerations_for_licensees
|
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|
491 |
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```
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834 |
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### Citation Information
|
835 |
-
|
836 |
-
Please cite the following papers when using this dataset.
|
837 |
-
|
838 |
-
```latex
|
839 |
-
@misc{fitzgerald2022massive,
|
840 |
-
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
|
841 |
-
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},
|
842 |
-
year={2022},
|
843 |
-
eprint={2204.08582},
|
844 |
-
archivePrefix={arXiv},
|
845 |
-
primaryClass={cs.CL}
|
846 |
-
}
|
847 |
-
|
848 |
-
@inproceedings{bastianelli-etal-2020-slurp,
|
849 |
-
title = "{SLURP}: A Spoken Language Understanding Resource Package",
|
850 |
-
author = "Bastianelli, Emanuele and
|
851 |
-
Vanzo, Andrea and
|
852 |
-
Swietojanski, Pawel and
|
853 |
-
Rieser, Verena",
|
854 |
-
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
|
855 |
-
month = nov,
|
856 |
-
year = "2020",
|
857 |
-
address = "Online",
|
858 |
-
publisher = "Association for Computational Linguistics",
|
859 |
-
url = "https://aclanthology.org/2020.emnlp-main.588",
|
860 |
-
doi = "10.18653/v1/2020.emnlp-main.588",
|
861 |
-
pages = "7252--7262",
|
862 |
-
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."
|
863 |
-
}
|
864 |
-
```
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