Metric-Free Learning Network with Dual Relations Propagation for Few-Shot Aspect Category Sentiment Analysis (2024)

Abstract: Few-shot Aspect Category Sentiment Analysis (ACSA) is a crucial task for aspect-based sentiment analysis, which aims to detect sentiment polarity for a given aspect category in a sentence with limited data. However, few-shot learning methods focus on distance metrics between the query and support sets to classify queries, heavily relying on aspect distributions in the embedding space. Thus, they suffer from overlapping distributions of aspect embeddings caused by irrelevant sentiment noise among sentences with multiple sentiment aspects, leading to misclassifications. To solve the above issues, we propose a metric-free method for few-shot ACSA, which models the associated relations among the aspects of support and query sentences by Dual Relations Propagation (DRP), addressing the passive effect of overlapping distributions. Specifically, DRP uses the dual relations (similarity and diversity) among the aspects of support and query sentences to explore intra-cluster commonality and inter-cluster uniqueness for alleviating sentiment noise and enhancing aspect features. Additionally, the dual relations are transformed from support-query to class-query to promote query inference by learning class knowledge. Experiments show that we achieve convincing performance on few-shot ACSA, especially an average improvement of 2.93% accuracy and 2.10% F1 score in the 3-way 1-shot setting.

## introduction aspect category sentiment analysis (acsa) @xcite @xcite @xcite ) is a * corresponding author. existing methods mostly rely on sufficient labeled data for each aspect category. though effective, they assume training and testing share a predefined set of aspects. however, this assumption becomes problematic in real-world scenarios with many unseen aspect categories. annotating abundant data for these emerging aspects poses a significant challenge, and there is a burden of retraining models for newly encountered aspects. therefore, generalizing experiences from seen aspect categories to unseen ones becomes crucial. this is where few-shot acsa becomes indispensable. existing few-shot learning methods (e.g., metalearning) mostly focus on distance metrics @xcite @xcite @xcite . among these methods, the prototypical network is a distance metric method well known because of its impressive performance. the prototypical network uses the support set to generate a prototype for each class and then classifies the query by measuring the distance (e.g., euclidean distance or cosine similarity) with different prototypes in the embedding space. ## proposed method the overall architecture of the proposed method is shown in figure 2 . broadly, the proposed method includes four components: relation graph construction, support-query relation propagation, class-query relation transformation, and training objective. here, we present the proposed method in detail. ## conclusion & future work we propose an effective metric-free method for the few-shot acsa task, which explicitly models the associated relations among aspects of query and support samples, addressing the passive effect of overlapping distributions caused by irrelevant sentiment noise in aspect distributions. specifically, the proposed method designs a fully connected relation graph to model the dual relations (similarity and diversity) among support and query nodes in the embedding space. with the relation graph, the proposed method uses the dual relations among nodes to explore intra-cluster commonality and inter-cluster uniqueness to alleviate irrelevant sentiment noise and enhance aspect features, eliminating the passive effect of overlapping distributions. additionally, the dual relations are transformed from support-query to class-query to guide query inference by learning sentiment class knowledge from the relation graph. experiments show that the proposed method outperforms strong baselines and obtains significant performance. the proposed method is not limited to few-shot acsa, and it can be applied to more complex tasks, e.g., fake news detection, text classification, and intention detection, since it could better enhance semantic textual similarity and diversity with ground truth texts. therefore, we will extend our method to these tasks in follow-up work.