In a modern spoken language understanding (SLU) system, the natural language understanding (NLU) module takes interpretations of a speech from the automatic speech recognition (ASR) module as the input. The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification. However, the ASR module might misrecognize some speeches and the first best interpretation could be erroneous and noisy. Solely relying on the first best interpretation could make the performance of downstream tasks non-optimal. To address this issue, we introduce a series of simple yet efficient models for improving the understanding of semantics of the input speeches by collectively exploiting the n-best speech interpretations from the ASR module.

中文翻译：

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通过利用 ASR N-best 假设提高口语理解

在现代口语理解 (SLU) 系统中，自然语言理解 (NLU) 模块将来自自动语音识别 (ASR) 模块的语音解释作为输入。NLU 模块通常在域和意图分类等下游任务中使用给定语音的第一个最佳解释。然而，ASR 模块可能会错误识别一些语音，第一个最好的解释可能是错误的和嘈杂的。仅依靠第一个最佳解释可能会使下游任务的性能变得非最佳。为了解决这个问题，我们引入了一系列简单而有效的模型，通过共同利用来自 ASR 模块的 n-best 语音解释来提高对输入语音语义的理解。