To handle these phenomena, we propose a Dialogue State Tracking with <br>Slot Connections (DST-SC) model to explicitly consider slot correlations across <br>completely different domains. Specially, we first apply a Slot Attention to <br>be taught a set of slot-specific features from the original dialogue and then integrate them using a <br>slot info sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang <br>author Yi Guo creator Siqi Zhu creator 2020-nov textual content <br>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing <br>(EMNLP) Association for Computational Linguistics Online conference publication Incompleteness of <br>area ontology and unavailability of some values <br>are two inevitable issues of dialogue state monitoring (DST).<br>In this paper, we suggest a new architecture to cleverly exploit ontology, <br>which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN.<br>SAS: Dialogue State Tracking by way of Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang creator Chencai Chen author Liang He <br>writer Zhou Yu author 2020-jul textual content Proceedings of the <br>58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics <br>Online conference publication Dialogue state tracker is accountable for inferring consumer <br>intentions by means of dialogue history. We suggest a Dialogue State Tracker with Slot <br>Attention and Slot Information Sharing (SAS) to cut back redundant information’s interference and improve lengthy dialogue context (...) web blog <a (...) Like191</a>
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