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    <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.</abstract>
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%0 Conference Proceedings
%T SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties
%A Kwon, Sang Yun
%A Bhatia, Gagan
%A Nagoudi, Elmoatez Billah
%A Alcoba Inciarte, Alcides
%A Abdul-mageed, Muhammad
%Y Scherrer, Yves
%Y Jauhiainen, Tommi
%Y Ljubešić, Nikola
%Y Nakov, Preslav
%Y Tiedemann, Jörg
%Y Zampieri, Marcos
%S Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F kwon-etal-2023-sidlr
%X 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.
%R 10.18653/v1/2023.vardial-1.24
%U https://aclanthology.org/2023.vardial-1.24
%U https://doi.org/10.18653/v1/2023.vardial-1.24
%P 241-250

Markdown (Informal)

[SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties](https://aclanthology.org/2023.vardial-1.24) (Kwon et al., VarDial 2023)

• SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties (Kwon et al., VarDial 2023)

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.