Spoken Language Understanding (SLU) systems typically consist of a set of machine learning models that operate in conjunction to produce an SLU hypothesis. The generated hypothesis is then sent to downstream components for further action. However, it is desirable to discard an incorrect hypothesis before sending it downstream. In this work, we present two designs for SLU hypothesis rejection modules: (i) scheme R1 that performs rejection on domain specific SLU hypothesis and, (ii) scheme R2 that performs rejection on hypothesis generated from the overall SLU system. Hypothesis rejection modules in both schemes reject/accept a hypothesis based on features drawn from the utterance directed to the SLU system, the associated SLU hypothesis and SLU confidence score. Our experiments suggest that both the schemes yield similar results (scheme R1: 2.5% FRR @ 4.5% FAR, scheme R2: 2.5% FRR @ 4.6% FAR), with the best performing systems using all the available features. We argue that while either of the rejection schemes can be chosen over the other, they carry some inherent differences which need to be considered while making this choice. Additionally, we incorporate ASR features in the rejection module (obtaining an 1.9% FRR @ 3.8% FAR) and analyze the improvements.

本文介绍了两种Spoken Language Understanding假设拒绝模块——R1和R2，它们分别基于领域特定的SLU假设和整个SLU系统生成假设的拒绝机制，并使用来自面向SLU系统的话语、相关SLU假设和SLU置信度的特征选择或接受假设。我们的实验表明，这两种方案都可以产生类似的结果，在所有可用功能下表现最佳。此外，我们还将ASR功能纳入拒绝模块，并分析了其改进。

口语理解系统中假设拒绝模块的设计考虑