论文标题
上下文敏感的生成网络，用于在对话状态跟踪中传递未知插槽值
Context-Sensitive Generation Network for Handing Unknown Slot Values in Dialogue State Tracking
论文作者
论文摘要
作为对话系统中的关键组成部分，对话状态跟踪起着重要作用。对话状态跟踪要处理未知插槽值的问题非常重要。据我们所知，几乎所有现有的方法都取决于指针网络来解决未知的插槽值问题。这些基于指针网络的方法通常具有一个隐藏的假设，即由于指针网络的特征，最多有一个未知的插槽值中的杂志插槽值。但是，通常，在未知的插槽值中有多个vocabulary单词，并且使现有方法的性能不佳。为了解决该问题，在本文中，我们提出了一个新颖的上下文敏感生成网络（CSG），该网络可以促进在生成未知插槽值时量不变单词的表示。广泛的实验表明，我们提出的方法的性能要比最先进的基线更好。
As a key component in a dialogue system, dialogue state tracking plays an important role. It is very important for dialogue state tracking to deal with the problem of unknown slot values. As far as we known, almost all existing approaches depend on pointer network to solve the unknown slot value problem. These pointer network-based methods usually have a hidden assumption that there is at most one out-of-vocabulary word in an unknown slot value because of the character of a pointer network. However, often, there are multiple out-of-vocabulary words in an unknown slot value, and it makes the existing methods perform bad. To tackle the problem, in this paper, we propose a novel Context-Sensitive Generation network (CSG) which can facilitate the representation of out-of-vocabulary words when generating the unknown slot value. Extensive experiments show that our proposed method performs better than the state-of-the-art baselines.
