  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.
