  Spoken Language Understanding (SLU) converts hypotheses from automatic speech
recognizer (ASR) into structured semantic representations. ASR recognition
errors can severely degenerate the performance of the subsequent SLU module. To
address this issue, word confusion networks (WCNs) have been used to encode the
input for SLU, which contain richer information than 1-best or n-best
hypotheses list. To further eliminate ambiguity, the last system act of
dialogue context is also utilized as additional input. In this paper, a novel
BERT based SLU model (WCN-BERT SLU) is proposed to encode WCNs and the dialogue
context jointly. It can integrate both structural information and ASR posterior
probabilities of WCNs in the BERT architecture. Experiments on DSTC2, a
benchmark of SLU, show that the proposed method is effective and can outperform
previous state-of-the-art models significantly.
