  The lack of publicly available evaluation data for low-resource languages
limits progress in Spoken Language Understanding (SLU). As key tasks like
intent classification and slot filling require abundant training data, it is
desirable to reuse existing data in high-resource languages to develop models
for low-resource scenarios. We introduce xSID, a new benchmark for
cross-lingual Slot and Intent Detection in 13 languages from 6 language
families, including a very low-resource dialect. To tackle the challenge, we
propose a joint learning approach, with English SLU training data and
non-English auxiliary tasks from raw text, syntax and translation for transfer.
We study two setups which differ by type and language coverage of the
pre-trained embeddings. Our results show that jointly learning the main tasks
with masked language modeling is effective for slots, while machine translation
transfer works best for intent classification.
