  Performance of spoken language understanding (SLU) can be degraded with
automatic speech recognition (ASR) errors. We propose a novel approach to
improve SLU robustness by randomly corrupting clean training text with an ASR
error simulator, followed by self-correcting the errors and minimizing the
target classification loss in a joint manner. In the proposed error simulator,
we leverage confusion networks generated from an ASR decoder without human
transcriptions to generate a variety of error patterns for model training. We
evaluate our approach on the DSTC10 challenge targeted for knowledge-grounded
task-oriented conversational dialogues with ASR errors. Experimental results
show the effectiveness of our proposed approach, boosting the knowledge-seeking
turn detection (KTD) F1 significantly from 0.9433 to 0.9904. Knowledge cluster
classification is boosted from 0.7924 to 0.9333 in Recall@1. After knowledge
document re-ranking, our approach shows significant improvement in all
knowledge selection metrics, from 0.7358 to 0.7806 in Recall@1, from 0.8301 to
0.9333 in Recall@5, and from 0.7798 to 0.8460 in MRR@5 on the test set. In the
recent DSTC10 evaluation, our approach demonstrates significant improvement in
knowledge selection, boosting Recall@1 from 0.495 to 0.7144 compared to the
official baseline. Our source code is released in GitHub
https://github.com/yctam/dstc10_track2_task2.git.
