This work explores the intrinsic limitations of the popular one-hot encoding method in classification of intents when detection of out-of-scope (OOS) inputs is required. Although recent work has shown that there can be significant improvements in OOS detection when the intent classes are represented as dense-vectors based on domain specific knowledge, we argue in this paper that such gains are more likely due to advantages of dense-vector to one-hot encoding methods in representing the complexity of the OOS space. We start by showing how dense-vector encodings can create OOS spaces with much richer topologies than one-hot encoding methods. We then demonstrate empirically, using four standard intent classification datasets, that knowledge-free, randomly generated dense-vector encodings of intent classes can yield massive, over 20% gains over one-hot encodings, and also outperform the previous, domain knowledge-based, SOTA of one of the datasets. We finish by describing a novel algorithm to search for good dense-vector encodings and present initial but promising experimental results of its use.

翻译：这项工作探索了在需要检测范围外输入时在意图分类中流行的单热编码方法的内在局限性。虽然最近的工作表明,当意图类别以基于特定领域知识的密度矢量为代表时,OOS的检测可以显著改进,但我们在本文中争辩说,由于密度矢量为一热编码方法的优点,这些增益更有可能代表OOS空间的复杂性。我们首先展示了密度矢量编码如何能以比单热编码方法更富的表层生成OOS空间。我们然后用四个标准意图分类数据集的经验展示了,即无知识、随机生成的意向类别密度矢量编码能够产生巨大收益,超过一热编码的20%,而且比一个数据集的先前、以域内知识为基础的SOTA还差。我们最后描述了一种新算法,以寻找良好的密度矢量编码,并展示其使用的初步但有希望的实验结果。