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Improving Dialectal Slot and Intent Detection With Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study

MCML Authors

Verena Blaschke

→ Group Barbara Plank
AI and Computational Linguistics

Barbara Plank

Prof. Dr.

Principal Investigator

AI and Computational Linguistics

Abstract

Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-resource languages generally perform well on SID. However, they struggle with dialectal data, where no standardized form exists and training data is scarce and costly to produce. We explore zero-shot transfer learning for SID, focusing on multiple Bavarian dialects, for which we release a new dataset for the Munich dialect. We evaluate models trained on auxiliary tasks in Bavarian, and compare joint multi-task learning with intermediate-task training. We also compare three types of auxiliary tasks: token-level syntactic tasks, named entity recognition (NER), and language modelling. We find that the included auxiliary tasks have a more positive effect on slot filling than intent classification (with NER having the most positive effect), and that intermediate-task training yields more consistent performance gains. Our best-performing approach improves intent classification performance on Bavarian dialects by 5.1 and slot filling F1 by 8.4 percentage points.

inproceedings

VarDial @COLING 2025

12th Workshop on NLP for Similar Languages, Varieties and Dialects at the The 31st International Conference on Computational Linguistics. Abu Dhabi, United Arab Emirates, Jan 19-24, 2025.