论文标题
基于BERT的快速，强大的对话状态跟踪器，用于模式引导的对话数据集
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset
论文作者
论文摘要
对话状态跟踪（DST）是针对目标对话系统的最关键模块之一。在本文中，我们介绍了FastSGT（快速模式指导跟踪器），这是一种基于BERT的快速且强大的模型，用于以目标对话系统中的状态跟踪。提出的模型是为架构引导的对话（SGD）数据集设计的，该数据集包含针对所有实体（包括用户意图，服务和插槽）的自然语言描述。该模型结合了两个随身携带程序，用于处理当前用户话语中未明确提及的值的提取。它还在某些解码器中使用多头注意力预测，以更好地建模编码器输出。在进行的实验中，我们将FASTSGT与SGD数据集的基线模型进行了比较。我们的模型在计算和内存消耗方面保持效率，同时显着提高准确性。此外，我们提供了消融研究，以测量模型的不同部分对其性能的影响。我们还展示了数据增强在不增加计算资源量的情况下提高准确性的有效性。
Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems. In this paper, we introduce FastSGT (Fast Schema Guided Tracker), a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The proposed model is designed for the Schema-Guided Dialogue (SGD) dataset which contains natural language descriptions for all the entities including user intents, services, and slots. The model incorporates two carry-over procedures for handling the extraction of the values not explicitly mentioned in the current user utterance. It also uses multi-head attention projections in some of the decoders to have a better modelling of the encoder outputs. In the conducted experiments we compared FastSGT to the baseline model for the SGD dataset. Our model keeps the efficiency in terms of computational and memory consumption while improving the accuracy significantly. Additionally, we present ablation studies measuring the impact of different parts of the model on its performance. We also show the effectiveness of data augmentation for improving the accuracy without increasing the amount of computational resources.
