论文标题
共同编码单词混乱网络和对话上下文与BERT有关口语理解
Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding
论文作者
论文摘要
口语理解（SLU）将自动语音识别器（ASR）的假设转换为结构化语义表示。 ASR识别错误可能会严重退化随后的SLU模块的性能。为了解决这个问题，已经使用了单词混乱网络（WCN）来编码SLU的输入，该输入包含比1好或n-pest假设列表更丰富的信息。为了进一步消除歧义，对话环境的最后一个系统行为也被用作附加输入。在本文中，提出了一个基于BERT的新型SLU模型（WCN-BERT SLU）共同编码WCN和对话环境。它可以整合BERT体系结构中WCN的结构信息和ASR后验概率。 SLU的基准DSTC2上的实验表明，该提出的方法是有效的，并且可以显着胜过先前的最新模型。
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.
