Abstract
Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology. In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains. The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora. Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in single-domain dialogue tracking tasks.
Abstract (translated)
强大的对话信念跟踪是维持高质量对话系统的关键组成部分。对话系统试图解决的任务变得越来越复杂，需要可扩展到多域，语义丰富的对话。然而，由于模型参数对对话本体的依赖性，大多数当前方法难以扩展域。在本文中，引入了一种新方法，充分利用对话话语和本体术语之间的语义相似性，允许跨域共享信息。评估是在最近收集的多域对话数据集上执行的，比当前可用的语料库大一个数量级。我们的模型展示了处理多域对话的强大能力，同时在单域对话跟踪任务中优于现有的最先进模型。
URL
https://arxiv.org/abs/1807.06517
