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.

本文提出了一种基于 BERT 的新型 SLU 模型（WCN-BERT SLU），其可以在 BERT 体系结构中整合 WCN 的结构信息和 ASR 后验概率，实验结果在 DSTC2 基准测试中表明该方法是有效的并且明显优于以前的最先进模型。