MASSIVE 1.1:一份包含52种语言的100万个用例的跨语言自然语言理解数据集

数据集概述

MASSIVE 1.1是一个包含超过100万个用例的平行数据集，涵盖了自然语言理解任务中的意图预测和槽位注释。这些用例涵盖了60个意图和55种槽位类型。MASSIVE是通过本地化SLURP数据集创建的，该数据集由一般智能语音助手的单次交互组成。

支持的任务和排行榜

该数据集可用于训练自然语言理解（NLU）模型的以下任务：

• 意图分类
• 多类分类
• 自然语言理解

语言

MASSIVE 1.1语料库包括来自52种语言的平行句子：

• 南非荷兰语-南非（af-ZA）
• 阿姆哈拉语-埃塞俄比亚（am-ET）
• 阿拉伯语-沙特阿拉伯（ar-SA）
• 阿塞拜疆语-阿塞拜疆（az-AZ）
• 孟加拉语-孟加拉国（bn-BD）
• 加泰罗尼亚语-西班牙（ca-ES）
• 中文-中国（zh-CN）
• 中文-台湾（zh-TW）
• 丹麦语-丹麦（da-DK）
• 德语-德国（de-DE）
• 希腊语-希腊（el-GR）
• 英语-美国（en-US）
• 西班牙语-西班牙（es-ES）
• 波斯语-伊朗（fa-IR）
• 芬兰语-芬兰（fi-FI）
• 法语-法国（fr-FR）
• 希伯来语-以色列（he-IL）
• 匈牙利语-匈牙利（hu-HU）
• 亚美尼亚语-亚美尼亚（hy-AM）
• 印度尼西亚语-印度尼西亚（id-ID）
• 冰岛语-冰岛（is-IS）
• 意大利语-意大利（it-IT）
• 日语-日本（ja-JP）
• 爪哇语-印度尼西亚（jv-ID）
• 格鲁吉亚语-格鲁吉亚（ka-GE）
• 高棉语-柬埔寨（km-KH）
• 韩语-韩国（ko-KR）
• 拉脱维亚语-拉脱维亚（lv-LV）
• 蒙古语-蒙古（mn-MN）
• 马来语-马来西亚（ms-MY）
• 缅甸语-缅甸（my-MM）
• 挪威语-挪威（nb-NO）
• 荷兰语-荷兰（nl-NL）
• 波兰语-波兰（pl-PL）
• 葡萄牙语-葡萄牙（pt-PT）
• 罗马尼亚语-罗马尼亚（ro-RO）
• 俄语-俄罗斯（ru-RU）
• 斯洛文尼亚语-斯洛文尼亚（sl-SL）
• 阿尔巴尼亚语-阿尔巴尼亚（sq-AL）
• 瑞典语-瑞典（sv-SE）
• 斯瓦希里语-肯尼亚（sw-KE）
• 印地语-印度（hi-IN）
• 卡纳达语-印度（kn-IN）
• 马拉雅拉姆语-印度（ml-IN）
• 泰米尔语-印度（ta-IN）
• 泰卢固语-印度（te-IN）
• 泰语-泰国（th-TH）
• 塔加路语-菲律宾（tl-PH）
• 土耳其语-土耳其（tr-TR）
• 乌尔都语-巴基斯坦（ur-PK）
• 越南语-越南（vi-VN）
• 威尔士语-英国（cy-GB）

使用HuggingFace加载数据集

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

数据集结构

数据实例

{
  "id": "0",
  "locale": "fr-FR",
  "partition": "test",
  "scenario": "alarm",
  "intent": "alarm_set",
  "utt": "réveille-moi à cinq heures du matin cette semaine",
  "annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]",
  "worker_id": "22",
  "slot_method": [
    { "slot": "time", "method": "translation" },
    { "slot": "date", "method": "translation" }
  ],
  "judgments": [
    {
      "worker_id": "22",
      "intent_score": 1,
      "slots_score": 1,
      "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": "0",
      "intent_score": 1,
      "slots_score": 1,
      "grammar_score": 4,
      "spelling_score": 2,
      "language_identification": "target"
    }
  ]
}

数据字段

id: 映射到 SLURP 集合中的原始ID。将其映射回SLURP en-US用例，此用例可用作本地化的基础。

locale：根据ISO-639-1和ISO-3166的语言和国家代码表示的语言。

partition：根据原始分割中的train、dev或test确定。

scenario：是用例的通用领域，即SLURP术语中的“scenario”。

intent：是领域内用例的具体意图，格式为{scenario}_{intent}。

utt：未经注释的原始用例文本。

annot_utt：从utt中提取的带有槽位注释的文本，格式为[{label} : {entity}]。

worker_id：完成用例本地化的MTurk工作者的ID（经过混淆）。工作者ID特定于某一语言环境，不跨语言环境映射。

slot_method：对于用例中的每个槽位，指示该槽位是翻译（即相同表达，在目标语言中表示）还是本地化（即不同表达，选择了更适合该语言环境的表达），还是未更改（即直接复制未经修改的原始en-US槽位值）。

judgments：对于本地化用例收集的每个判断，有6个键。judgment_id是完成判断的MTurk工作者的ID（经过混淆）。工作者ID特定于某一语言环境，不跨语言环境映射，但在本地化任务和判断任务之间是一致的，例如，上述示例中的判断工作者ID 32可能会出现为不同的de-DE用例的本地化工作者ID，在这种情况下，它将是同一工作者。

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

数据拆分

个人和敏感信息

该语料库不包含个人或敏感信息。

附加信息

数据集贡献者

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

SLURP：Bastianelli、Emanuele、Vanzo、Andrea和Swietojanski、Pawel和Rieser、Verena。

Hugging Face 上传和集成：Labrak Yanis（与原始语料库无关）

许可信息

Copyright Amazon.com Inc. or its affiliates.

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引用信息

在使用此数据集时，请引用以下论文。

@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."
}