Recently, deep end-to-end learning has been studied for intent classification
in Spoken Language Understanding (SLU). However, end-to-end models require a
large amount of speech data with intent labels, and highly optimized models are
generally sensitive to the inconsistency between the training and evaluation
conditions. Therefore, a natural language understanding approach based on
Automatic Speech Recognition (ASR) remains attractive because it can utilize a
pre-trained general language model and adapt to the mismatch of the speech
input environment. Using this module-based approach, we improve a noisy-channel
model to handle transcription inconsistencies caused by ASR errors. We propose
a two-stage method, Contrastive and Consistency Learning (CCL), that correlates
error patterns between clean and noisy ASR transcripts and emphasizes the
consistency of the latent features of the two transcripts. Experiments on four
benchmark datasets show that CCL outperforms existing methods and improves the
ASR robustness in various noisy environments. Code is available at
this https URL

基于语音识别的自然语言理解方法和两阶段的对比一致性学习模型能够提高在不同噪声环境下的自动语音识别的鲁棒性。

基于对比学习和一致性学习的神经噪声通道模型在口语理解中的应用

Contrastive and Consistency Learning for Neural Noisy-Channel Model in  Spoken Language Understanding

We present a document compression system that uses a hierarchical
noisy-channel model of text production. Our compression system first
automatically derives the syntactic structure of each sentence and the overall
discourse structure of the text given as input. The system then uses a
statistical hierarchical model of text production in order to drop
non-important syntactic and discourse constituents so as to generate coherent,
grammatical document compressions of arbitrary length. The system outperforms
both a baseline and a sentence-based compression system that operates by
simplifying sequentially all sentences in a text. Our results support the claim
that discourse knowledge plays an important role in document summarization.

我们提出了一种使用层级噪声信道模型的文档压缩系统，该系统可以自动获取输入文本的句法结构和总体话语结构，并使用统计模型生成连贯的、语法正确的文档摘要。我们的实验结果支持话语知识在文档摘要中发挥重要作用的观点。