Entity tags in human-machine dialog are integral to natural language
understanding (NLU) tasks in conversational assistants. However, current
systems struggle to accurately parse spoken queries with the typical use of
text input alone, and often fail to understand the user intent. Previous work
in linguistics has identified a cross-language tendency for longer speech
pauses surrounding nouns as compared to verbs. We demonstrate that the
linguistic observation on pauses can be used to improve accuracy in
machine-learnt language understanding tasks. Analysis of pauses in French and
English utterances from a commercial voice assistant shows the statistically
significant difference in pause duration around multi-token entity span
boundaries compared to within entity spans. Additionally, in contrast to
text-based NLU, we apply pause duration to enrich contextual embeddings to
improve shallow parsing of entities. Results show that our proposed novel
embeddings improve the relative error rate by up to 8% consistently across
three domains for French, without any added annotation or alignment costs to
the parser.

本文阐述语言学中名词周围的语音停顿可以用于改善机器学习语言理解任务；作者的提出的一种新颖的语义嵌入模型能够用于增强浅层实体分析，结果显示此模型相比传统方法能够将误差率提高 8%。