Dialogue state tracking is an essential part of goal-oriented dialogue systems, while most of these state tracking models often fail to handle unseen services. In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach. The proposed multi-pass model shares a single encoder between the domain information and dialogue utterance. The domain's description represents the query and the dialogue utterance serves as the context. The model improves performance on unseen services by at least 1.6x compared to single-pass baseline models on the SGD dataset. SGD-QA shows competitive performance compared to state-of-the-art multi-pass models while being significantly more efficient in terms of memory consumption and training performance. We provide a thorough discussion on the model with ablation study and error analysis.
翻译:对话状态跟踪是面向目标的对话系统的一个基本部分,而大多数这类国家跟踪模式往往无法处理不可见的服务。在本文件中,我们提议SGD-QA,这是基于答问方法的一种简单和可扩展的系统指导对话状态跟踪模式。提议的多通模式在域信息和对话表达之间共用一个编码器。域描述代表了查询和对话表达作为背景。该模式与SGD数据集的单一通用基准模式相比,至少改善了1.6x的未见服务绩效。SGD-QA显示与最先进的多通程模式相比,其竞争性业绩,同时在记忆消耗和培训表现方面效率要高得多。我们用通缩研究和误差分析对模型进行了透彻的讨论。