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
this https URL.

该研究的主要目的是通过使用自动语音识别 (ASR) 中的错误模拟器实现对清洁训练文本进行随机破坏，从而提高口头语言理解 (SLU) 的鲁棒性，结果表明该方法可以显著提高知识选择及分类产生的结果。

具有 ASR 错误的对话式交互中的稳健非结构化知识访问

Robust Unstructured Knowledge Access in Conversational Dialogue with ASR Errors

This paper summarizes our submission to Task 2 of the second track of the
10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded
Task-oriented Dialogue Modeling on Spoken Conversations". Similar to the
previous year's iteration, the task consists of three subtasks: detecting
whether a turn is knowledge seeking, selecting the relevant knowledge document
and finally generating a grounded response. This year, the focus lies on
adapting the system to noisy ASR transcripts. We explore different approaches
to make the models more robust to this type of input and to adapt the generated
responses to the style of spoken conversations. For the latter, we get the best
results with a noisy channel model that additionally reduces the number of
short and generic responses. Our best system achieved the 1st rank in the
automatic and the 3rd rank in the human evaluation of the challenge.

本文介绍了我们在第十届对话系统技术挑战赛第二轨知识引导的任务导向对话建模中的提交内容。在适应 ASR 转录的噪声方面探索不同的方法来使得模型更加健壮，并采用噪声通道模型来适应口语交流的风格。我们的最佳系统在挑战的自动和人工评估中分别排名第一和第三。