  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.
