Despite recent progress in Natural Language Understanding (NLU), the creation
of multilingual NLU systems remains a challenge. It is common to have NLU
systems limited to a subset of languages due to lack of available data. They
also often vary widely in performance. We launch a three-phase approach to
address the limitations in NLU and help propel NLU technology to new heights.
We release a 52 language dataset called the Multilingual Amazon SLU resource
package (SLURP) for Slot-filling, Intent classification, and Virtual assistant
Evaluation, or MASSIVE, in an effort to address parallel data availability for
voice assistants. We organize the Massively Multilingual NLU 2022 Challenge to
provide a competitive environment and push the state-of-the art in the
transferability of models into other languages. Finally, we host the first
Massively Multilingual NLU workshop which brings these components together. The
MMNLU workshop seeks to advance the science behind multilingual NLU by
providing a platform for the presentation of new research in the field and
connecting teams working on this research direction. This paper summarizes the
dataset, workshop and the competition and the findings of each phase.

本文介绍了一个三阶段的方案，旨在解决自然语言理解中多语言系统的限制，并推进 NLU 技术发展。该方案发布了一个包含 52 种语言数据集的 Multilingual Amazon SLU 资源包，并组织了 Massively Multilingual NLU 2022 Challenge 和首个 Massively Multilingual NLU 工作坊，以此推进多语言 NLU 研究。