Users of spoken dialogue systems (SDS) expect high quality interactions across a wide range of diverse topics. However, the implementation of SDS capable of responding to every conceivable user utterance in an informative way is a challenging problem. Multi-domain SDS must necessarily identify and deal with out-of-domain (OOD) utterances to generate appropriate responses as users do not always know in advance what domains the SDS can handle. To address this problem, we extend the current state-of-the-art in multi-domain SDS by estimating the topic of OOD utterances using external knowledge representation from Wikipedia. Experimental results on real human-to-human dialogues showed that our approach does not degrade domain prediction performance when compared to the base model. But more significantly, our joint training achieves more accurate predictions of the nearest Wikipedia article by up to about 30% when compared to the benchmarks.

翻译：口声对话系统的用户期望在各种不同的专题上进行高质量的互动。然而,实施能够以信息方式对每一个可以想象的用户的言论作出反应的SDS是一个具有挑战性的问题。多域 SDS必须确定和处理外域(OOOD)的言论,因为用户并不总是事先知道SDS能够处理哪些领域,因此他们不一定会做出适当的反应。为了解决这个问题,我们利用维基百科的外部知识来估计OOOD言论的主题,从而扩大目前多域SDS的最新水平。 真正的人与人对话的实验结果表明,与基本模型相比,我们的方法不会降低域预测的性能。但更重要的是,我们的联合培训比基准更准确地预测最近的维基百科文章,比基准高出大约30%。