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 wella color touch 77 45 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 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 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 caruso rhodiola 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.