This paper describes our system for the low-resource domain adaptation track (Track 3) in Spoken Language Understanding Grand Challenge, which is a part of ICASSP Signal Processing Grand Challenge 2023. In the track, we adopt a pipeline approach of ASR and NLU. For ASR, we fine-tune Whisper for each domain with upsampling. For NLU, we fine-tune BART on all the Track3 data and then on low-resource domain data. We apply masked LM (MLM) -based data augmentation, where some of input tokens and corresponding target labels are replaced using MLM. We also apply a retrieval-based approach, where model input is augmented with similar training samples. As a result, we achieved exact match (EM) accuracy 63.3/75.0 (average: 69.15) for reminder/weather domain, and won the 1st place at the challenge.
@article{arxiv.2305.01194,
title = {The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge},
author = {Hayato Futami and Jessica Huynh and Siddhant Arora and Shih-Lun Wu and Yosuke Kashiwagi and Yifan Peng and Brian Yan and Emiru Tsunoo and Shinji Watanabe},
journal= {arXiv preprint arXiv:2305.01194},
year = {2023}
}