论文标题
神经对话状态跟踪具有时间表达网络
Neural Dialogue State Tracking with Temporally Expressive Networks
论文作者
论文摘要
对话状态跟踪（DST）是口语对话系统的重要组成部分。现有的DST模型要么忽略对话转弯之间的时间特征依赖项，要么无法在对话中明确对时间状态依赖性建模。在这项工作中，我们提出了时间表达网络（十），以共同对DST中的两种时间依赖性进行建模。十个模型利用了复发网络和概率图形模型的力量。在评估标准数据集上，十个被证明可以有效地提高转弯状态预测和状态聚合的准确性。
Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to be effective in improving the accuracy of turn-level-state prediction and the state aggregation.
