Few-Shot Multi-Label Aspect Category Detection Utilizing Prototypical
  Network with Sentence-Level Weighting and Label Augmentation
Zeyu Wang Mizuho Iwaihara
Multi-label aspect category detection is intended to detect multiple aspect
categories occurring in a given sentence. Since aspect category detection often
suffers from limited datasets and data sparsity, the prototypical network with
attention mechanisms has been applied for few-shot aspect category detection.
Nevertheless, most of the prototypical networks used so far calculate the
prototypes by taking the mean value of all the instances in the support set.
This seems to ignore the variations between instances in multi-label aspect
category detection. Also, several related works utilize label text information
to enhance the attention mechanism. However, the label text information is
often short and limited, and not specific enough to discern categories. In this
paper, we first introduce support set attention along with the augmented label
information to mitigate the noise at word-level for each support set instance.
Moreover, we use a sentence-level attention mechanism that gives different
weights to each instance in the support set in order to compute prototypes by
weighted averaging. Finally, the calculated prototypes are further used in
conjunction with query instances to compute query attention and thereby
eliminate noises from the query set. Experimental results on the Yelp dataset
show that our proposed method is useful and outperforms all baselines in four
different scenarios.