  Intent detection and slot filling are critical tasks in spoken and natural
language understanding for task-oriented dialog systems. In this work we
describe our participation in the slot and intent detection for low-resource
language varieties (SID4LR; Aepli et al. (2023)). We investigate the slot and
intent detection (SID) tasks using a wide range of models and settings. Given
the recent success of multitask-prompted finetuning of large language models,
we also test the generalization capability of the recent encoder-decoder model
mT0 (Muennighoff et al., 2022) on new tasks (i.e., SID) in languages they have
never intentionally seen. We show that our best model outperforms the baseline
by a large margin (up to +30 F1 points) in both SID tasks
