论文标题
与自适应决策边界的深度开放意图分类
Deep Open Intent Classification with Adaptive Decision Boundary
论文作者
论文摘要
开放意图分类是对话系统中一项具有挑战性的任务。一方面，它应确保已知意图识别的质量。另一方面，它需要在没有先验知识的情况下检测开放的（未知）意图。当前的模型在找到适当的决策边界方面受到限制，以平衡已知意图和开放意图的性能。在本文中，我们提出了一种后处理方法，以学习自适应决策边界（ADB）进行开放意图分类。我们首先利用标记的已知意图样本来预先培训模型。然后，我们会借助训练有素的特征自动学习每个已知类别的自适应球形决策边界。具体来说，我们提出了一个新的损失功能，以平衡经验风险和开放空间风险。我们的方法不需要开放的意图样本，并且不需要修改模型体系结构。此外，我们的方法令人惊讶地不敏感，标有较低的数据和更少的已知意图。在三个基准数据集上进行的广泛实验表明，与最先进的方法相比，我们的方法可产生重大改进。这些代码在https://github.com/thuiar/adaptive-decision-boundary上发布。
Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods. The codes are released at https://github.com/thuiar/Adaptive-Decision-Boundary.
