Zero-Shot Slot and Intent Detection in Low-Resource Languages
This addresses improving natural language understanding for task-oriented dialog systems in low-resource languages, but is incremental as it applies existing methods to new tasks and data.
The paper tackled slot and intent detection in low-resource languages by testing models including mT0, resulting in a best model that outperformed the baseline by up to +30 F1 points.
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