Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification and slot filling tasks. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. To interpret the state-of-the-art trends, we provide multiple tables that describe and summarise past research along different dimensions, including the types of features, base approaches, and dataset domain used.
翻译:意图的分类和空缺填充是自然语言理解的两个关键任务。传统上,这两项任务被认为是独立进行。但最近,意图的分类和空缺填充的联合模式取得了最新业绩,并证明这两项任务之间存在牢固的关系。本条款汇编了过去在自然语言理解方面所做的工作,特别是共同意图的分类和空缺填充。我们观察了迄今为止这项研究的三个里程碑:有意探测以辨别发言者的意图、在发言/文本中标出每个字标的空缺填充,以及最后,共同意图的分类和空缺填充任务。我们在本条中描述了意图分类和空缺填充方面的趋势、方法、问题、数据集、评价指标。我们还讨论了代表性的业绩价值,介绍了共同的任务,并按先前的工作说明了今后的工作。为解释最新趋势,我们提供了多张表格,描述和总结了不同层面的过去研究,包括特征类型、基准方法和所使用的数据集域。