The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition
This work addresses the problem of limited multilingual data and performance variability in NLU systems, primarily for researchers and developers in AI and language technology, but it is incremental as it builds on existing efforts.
The authors tackled the challenge of multilingual natural language understanding by releasing a 52-language dataset (MASSIVE), organizing a competition, and hosting a workshop to advance research and model transferability across languages.
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.