A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
This work addresses the problem of limited labeled data for aspect-based sentiment analysis, offering a method that improves performance in few-shot and zero-shot settings, though it is incremental in nature.
The paper tackles few-shot aspect-based sentiment analysis by using weak supervision on unlabeled data to adapt a pre-trained model, achieving a 15.84% absolute F1 improvement in few-shot scenarios and outperforming previous state-of-the-art in zero-shot tasks.
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA datasets, before and after fine-tuning. Our proposed method preserves the full fine-tuning performance while showing significant improvements (15.84% absolute F1) in the few-shot learning scenario for the harder tasks. In zero-shot (i.e., without fine-tuning), our method outperforms the previous state of the art on the aspect extraction sentiment classification (AESC) task and is, additionally, capable of performing the harder aspect sentiment triplet extraction (ASTE) task.