  We propose two methods to capture relevant history information in a
multi-turn dialogue by modeling inter-speaker relationship for spoken language
understanding (SLU). Our methods are tailored for and therefore compatible with
XLNet, which is a state-of-the-art pretrained model, so we verified our models
built on the top of XLNet. In our experiments, all models achieved higher
accuracy than state-of-the-art contextual SLU models on two benchmark datasets.
Analysis on the results demonstrated that the proposed methods are effective to
improve SLU accuracy of XLNet. These methods to identify important dialogue
history will be useful to alleviate ambiguity in SLU of the current utterance.
