  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.
