论文标题
为面向任务的对话状态生成建模长篇小说
Modeling Long Context for Task-Oriented Dialogue State Generation
论文作者
论文摘要
基于最近提出的可转让对话状态发电机（贸易），该对话是通过引人注目的对话环境中的对话状态，我们提出了一个多任务学习模型，采用简单而有效的话语标记技术和双向语言模型作为辅助任务，用于以任务为导向的对话对话对话。通过使模型能够更好地表示长度对话上下文的更好表示，我们的方法试图解决一个问题，即当输入对话对话上下文序列长时间时，基线的性能会显着下降。在我们的实验中，我们提出的模型比基线实现了7.03％的相对改善，在MultiWoz 2.0数据集上建立了新的最新联合目标准确性52.04％。
Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. By enabling the model to learn a better representation of the long dialogue context, our approaches attempt to solve the problem that the performance of the baseline significantly drops when the input dialogue context sequence is long. In our experiments, our proposed model achieves a 7.03% relative improvement over the baseline, establishing a new state-of-the-art joint goal accuracy of 52.04% on the MultiWOZ 2.0 dataset.
