论文标题
模式编码可转让的对话状态跟踪
Schema Encoding for Transferable Dialogue State Tracking
论文作者
论文摘要
对话状态跟踪（DST）是面向任务对话系统的必不可少的子任务。最近的工作集中在DST的深层神经模型上。但是，神经模型需要大型数据集进行培训。此外，将它们应用于另一个域需要一个新的数据集，因为通常训练神经模型以模仿给定的数据集。在本文中，我们提出了用于转移对话状态跟踪（SETDST）的模式编码，这是一种有效传输到新领域的神经DST方法。即使在目标域上的数据集很少，可转移的DST也可以协助对话系统的开发。我们不仅使用模式编码器来模仿数据集，还可以理解数据集的模式。我们旨在通过编码新模式并将其用于多域设置上的DST来将模型转移到新域。结果，SET-DST在Multiwoz 2.1上提高了关节精度1.46点。
Dialogue state tracking (DST) is an essential sub-task for task-oriented dialogue systems. Recent work has focused on deep neural models for DST. However, the neural models require a large dataset for training. Furthermore, applying them to another domain needs a new dataset because the neural models are generally trained to imitate the given dataset. In this paper, we propose Schema Encoding for Transferable Dialogue State Tracking (SETDST), which is a neural DST method for effective transfer to new domains. Transferable DST could assist developments of dialogue systems even with few dataset on target domains. We use a schema encoder not just to imitate the dataset but to comprehend the schema of the dataset. We aim to transfer the model to new domains by encoding new schemas and using them for DST on multi-domain settings. As a result, SET-DST improved the joint accuracy by 1.46 points on MultiWOZ 2.1.
