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...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Sang Yun Kwon, Bhatia, Gagan, El Moatez Billah Nagoudi, Alcides Alcoba Inciarte, Abdul-Mageed, Muhammad
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.04.2023
Subjects
Coders
Encoders-Decoders
Languages
Online AccessGet full text

Cover

More Information
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
ISSN:2331-8422