SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties
Sang Yun Kwon, Gagan Bhatia, Elmoatez Billah Nagoudi, Alcides Alcoba Inciarte, Muhammad Abdul-mageed
Abstract
Intent detection and slot filling are two critical tasks in spoken and natural language understandingfor task-oriented dialog systems. In this work, we describe our participation in 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 promptedfinetuning of the 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.- Anthology ID:
- 2023.vardial-1.24
- Volume:
- Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 241–250
- Language:
- URL:
- https://aclanthology.org/2023.vardial-1.24
- DOI:
- Cite (ACL):
- Sang Yun Kwon, Gagan Bhatia, Elmoatez Billah Nagoudi, Alcides Alcoba Inciarte, and Muhammad Abdul-mageed. 2023. SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties. In Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023), pages 241–250, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties (Kwon et al., VarDial 2023)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2023.vardial-1.24.pdf