Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection (2022)

Abstract: 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.

## introduction aspect category detection (acd) is an important subtask of fine-grained sentiment analysis @xcite , which aims to detect the aspect categories mentioned in a review sentence from a predefined set of aspect categories. for example, given the sentence "the service is good although rooms are pretty expensive.", the acd task is to detect two aspect categories from the sentence, respectively service and price. obviously, the acd belongs to a multi-label classification problem. recently, with the development of deep learning technique, a great number of neural models (2) food was over priced, but okay not great. ## notations and background in this section, we first present the task formalization of fs-acd and then give brief introductions to the background. ## conclusion in this paper, we propose a novel label-driven denoising framework (ldf) to alleviate the noise problems for the fs-acd task. specifically, we design two reasonable components: label-guided attention strategy and label-weighted contrastive loss, which aim to produce a better prototype for each class and distinguish similar prototypes. results from numerous experiments indicate that our framework ldf achieves better performance than other state-of-the-art methods.