论文标题
特定于任务对话法的跨语性方法识别
Cross-lingual Approaches for Task-specific Dialogue Act Recognition
论文作者
论文摘要
在本文中，我们利用跨语性模型来实现对话法案的识别，以识别少数注释的特定任务。我们为对话行为设计了一种转移学习方法，并在两种不同的目标语言和域上验证它。我们使用CNN和多头自我发项模型来计算对话对话嵌入，并表明通过结合所有传输信息来源来获得最佳结果。我们进一步证明，所提出的方法显着优于相关的跨语性DA识别方法。
In this paper we exploit cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations. We design a transfer learning approach for dialogue act recognition and validate it on two different target languages and domains. We compute dialogue turn embeddings with both a CNN and multi-head self-attention model and show that the best results are obtained by combining all sources of transferred information. We further demonstrate that the proposed methods significantly outperform related cross-lingual DA recognition approaches.
