Heuristic-enhanced Candidates Selection strategy for GPTs tackle Few-Shot Aspect-Based Sentiment Analysis
This work addresses the problem of few-shot aspect-based sentiment analysis for natural language processing applications, representing an incremental improvement by hybridizing existing methods.
The paper tackled the challenge of Few-Shot Aspect-Based Sentiment Analysis (FSABSA) by proposing a two-stage model called All in One (AiO) that combines Pre-trained Language Models (PLMs) and Generative Pre-trained Transformers (GPTs), resulting in improved performance over methods using GPTs directly, as demonstrated on five benchmark datasets.
Few-Shot Aspect-Based Sentiment Analysis (FSABSA) is an indispensable and highly challenging task in natural language processing. However, methods based on Pre-trained Language Models (PLMs) struggle to accommodate multiple sub-tasks, and methods based on Generative Pre-trained Transformers (GPTs) perform poorly. To address the above issues, the paper designs a Heuristic-enhanced Candidates Selection (HCS) strategy and further proposes All in One (AiO) model based on it. The model works in a two-stage, which simultaneously accommodates the accuracy of PLMs and the generalization capability of GPTs. Specifically, in the first stage, a backbone model based on PLMs generates rough heuristic candidates for the input sentence. In the second stage, AiO leverages LLMs' contextual learning capabilities to generate precise predictions. The study conducted comprehensive comparative and ablation experiments on five benchmark datasets. The experimental results demonstrate that the proposed model can better adapt to multiple sub-tasks, and also outperforms the methods that directly utilize GPTs.