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Amazon releases 51-language dataset for language understanding

MASSIVE dataset and Massively Multilingual NLU (MMNLU-22) competition and workshop will help researchers scale natural-language-understanding technology to every language on Earth.

By Jack G. M. FitzGerald
April 20, 2022
6 min read
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Imagine that all people around the world could use voice AI systems such as Alexa in their native tongues.

Multilingual Alexa.png
The MASSIVE dataset is a step toward the creation of multilingual natural-language-understanding models that can generalize easily to new languages.

One promising approach to realizing this vision is massively multilingual natural-language understanding (MMNLU), a paradigm in which a single machine learning model can parse and understand inputs from many typologically diverse languages. By learning a shared data representation that spans languages, the model can transfer knowledge from languages with abundant training data to those in which training data is scarce.

Today we are pleased to make three announcements related to MMNLU.

First, we are releasing a new dataset called MASSIVE, which is composed of one million labeled utterances spanning 51 languages, along with open-source code, which provides examples of how to perform massively multilingual NLU modeling and allows practitioners to re-create baseline results for intent classification and slot filling that are presented in our paper..

polyglot_diagram.png
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Second, we are launching a new competition using the MASSIVE dataset called Massively Multilingual NLU 2022 (MMNLU-22).

And third, we will cohost a workshop at EMNLP 2022 in Abu Dhabi and online, also called Massively Multilingual NLU 2022, which will highlight the results from the competition and include presentations from invited speakers and oral and poster sessions from submitted papers on multilingual natural-language processing (NLP).

“We are very excited to share this large multilingual dataset with the worldwide language research community,” says Prem Natarajan, vice president of Alexa AI Natural Understanding. “We hope that this dataset will enable researchers across the world to drive new advances in multilingual language understanding that expand the availability and reach of conversational-AI technologies.”

The MASSIVE dataset

MASSIVE is a parallel dataset, meaning that every utterance is given in all 51 languages. This enables models to learn shared representations of utterances with the same intents, regardless of language, facilitating cross-linguistic training on natural-language-understanding (NLU) tasks. It also allows for adaptation to other NLP tasks such as machine translation, multilingual paraphrasing, new linguistic analyses of imperative morphologies, and more.

The architecture of our slot-filling and intent classification model
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NLU — a subdiscipline of NLP — is a machine's ability to understand the meaning of a text and identify the relevant entities. For instance, given the utterance “What is the temperature in New York?”, an NLU model might classify the intent as “weather_query” and recognize relevant entities as “weather_descriptor: temperature” and “place_name: new york.”

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Our particular focus is on NLU as a component of spoken-language understanding (SLU), in which audio is converted to text before NLU is performed. Although SLU-based virtual assistants like Alexa have made major capability advances in the past decade, academic and industrial NLU efforts worldwide are still limited to a small subset of the world's 7,000+ languages. One difficulty in creating massively multilingual NLU models is the lack of labeled data for training and evaluation — particularly data that is realistic for a given task and natural for a given language. High naturalness typically requires human vetting, which is often costly.

MASSIVE — Multilingual Amazon SLURP (SLU resource package) for Slot Filling, Intent Classification, and Virtual-Assistant Evaluation — contains one million realistic, parallel, labeled virtual-assistant text utterances spanning 51 languages, 18 domains, 60 intents, and 55 slots. MASSIVE was created by tasking professional translators to localize or translate the English-only SLURP dataset into 50 typologically diverse languages from 29 genera, including low-resource languages.

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

We have released a paper describing the dataset and presenting baseline modeling results on XLM-R and mT5 models. Tools for the dataset, as well as the modeling code used for our baseline results, are available in our Github repository. MASSIVE is licensed under the CC BY 4.0 license, encouraging its broadest possible use across academia and industry.

MMNLU competition and workshop

The MASSIVE leaderboard and the Massively Multilingual NLU 2022 competition, hosted on eval.ai, are composed of two tasks. In the first, called MMNLU-22-Full, each competitor trains and tests a single model on all 51 languages of the full MASSIVE dataset. In the second task, called MMNLU-22-ZeroShot, each competitor fine-tunes a pretrained model only with English-labeled data and tests it on all 50 non-English languages.

Locale-agnostic_architecture.png._CB462220682_.png
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This assesses the model’s ability to generalize to new languages, an important consideration given the number of languages around the world for which there is little-to-no labeled data. Zero-shot learning is a key technology for scaling NLU technology to many more low-resource languages worldwide.

The permanent MASSIVE leaderboard has been launched, and on July 25 the Massively Multilingual NLU 2022 evaluation split will be released. Participants will then have until August 8 to perform inference on the evaluation set and submit their predictions, which will be used to determine the winners. Winners will be invited to give an oral presentation at the Massively Multilingual NLU 2022 workshop.

The Massively Multilingual NLU 2022 workshop is collocated with EMNLP 2022 and will take place on either December 7 or 8, both in person in Abu Dhabi and online. Paper submissions spanning the breadth of multilingualism in NLU are sought, and the first call for papers will be released soon. The workshop will feature speakers on various topics related to multilingualism and NLU, as well as talks from the top performers from the MMNLU-22 competition.

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Let’s scale natural-language-understanding technology to every language on Earth. Come build with us!

Acknowledgments

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, and Prem Natarajan for core dataset contributions; Andrew Turner for product and program management; Anna-Karin Johansson for vendor management; Saleh Soltan for text-to-text modeling discussions; Anne Yoder, Zheng Xie, Adeetee Bhide, Misa Sunaga, Trang Doan, and Satyam Dwivedi for program management and language expertise; Wayne Blossom, Brendan Egan, Columbine Marshall, Todd Tieuli, and Augusta Niles for creating the hidden evaluation split of the dataset; Jack FitzGerald, Kay Rottmann, Julia Hirschberg, Anna Rumshisky, and Mohit Bansal for workshop organization; and Charith Peris and Jack FitzGerald for leaderboard and competition setup.

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About the Author
Jack G. M. FitzGerald
Jack G. M. FitzGerald is a senior applied scientist in the Artificial General Intelligence (AGI) organization at Amazon and has a background in machine learning, physics, and tech leadership. He currently focuses on large-language-model pretraining and adaptation, multilingual modeling, optimized distributed training, and efficient modeling operations.

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Applied Scientist
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
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