论文标题
标签驱动的多标签的denoising框架，几个射击方面类别检测
Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection
论文作者
论文摘要
多标签少数射击方面类别检测（FS-ACD）是基于方面的情感分析的新子任务，旨在通过有限的培训实例准确地检测方面类别。最近，主要的作品使用原型网络来完成此任务，并采用注意力机制从句子中提取方面类别的关键字，以为每个方面生成原型。但是，它们仍然遇到严重的噪声问题：（1）由于缺乏足够的监督数据，以前的方法很容易捕获与当前方面类别无关的嘈杂词，这在很大程度上影响了生成的原型的质量； （2）语义上关闭的方面类别通常会产生相似的原型，这些原型互相嘈杂，并认真对待分类器。在本文中，我们求助于各个方面的标签信息，以解决上述问题，并提出了一种新型标签驱动的Denoising框架（LDF）。广泛的实验结果表明，我们的框架比其他最先进的方法更好。
Multi-Label Few-Shot Aspect Category Detection (FS-ACD) is a new sub-task of aspect-based sentiment analysis, which aims to detect aspect categories accurately with limited training instances. Recently, dominant works use the prototypical network to accomplish this task, and employ the attention mechanism to extract keywords of aspect category from the sentences to produce the prototype for each aspect. However, they still suffer from serious noise problems: (1) due to lack of sufficient supervised data, the previous methods easily catch noisy words irrelevant to the current aspect category, which largely affects the quality of the generated prototype; (2) the semantically-close aspect categories usually generate similar prototypes, which are mutually noisy and confuse the classifier seriously. In this paper, we resort to the label information of each aspect to tackle the above problems, along with proposing a novel Label-Driven Denoising Framework (LDF). Extensive experimental results show that our framework achieves better performance than other state-of-the-art methods.
