Few-Shot Methods for Aspect-Level Sentiment Analysis

Few-Shot Methods for Aspect-Level Sentiment Analysis

Blog Article

In this paper, we explore the approaches to the problem of cross-domain few-shot classification of sentiment aspects.By cross-domain few-shot, we mean a setting where the model is trained on large data Vegetable Brush in one domain (for example, hotel reviews) and is intended to perform on another (for example, restaurant reviews) with only a few labelled examples in the target domain.We start with pre-trained monolingual language models.Using the Polish language dataset AspectEmo, we compare Measuring Jug model training using standard gradient-based learning to a zero-shot approach and two dedicated few-shot methods: ProtoNet and NNShot.We find both dedicated methods much superior to both gradient learning and zero-shot setup, with a small advantage held by NNShot.

Overall, we find few-shot to be a compelling alternative, achieving a surprising amount of performance compared to gradient training on full-size data.