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。

2022年大规模多语言自然语言理解工作坊和竞赛