Zero-Shot Slot and Intent Detection in Low-Resource Languages

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 a...

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Bibliographic Details
Main Authors Kwon, Sang Yun, Bhatia, Gagan, Nagoudi, El Moatez Billah, Inciarte, Alcides Alcoba, Abdul-Mageed, Muhammad
Format Journal Article
LanguageEnglish
Published 26.04.2023
Subjects
Computer Science - Computation and Language
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Summary: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
DOI:10.48550/arxiv.2304.13292