Abstract
This paper explores the challenges posed by aspect-based sentiment classification (ABSC) within pretrained language models (PLMs), with a particular focus on contextualization and hallucination issues. In order to tackle these challenges, we introduce CARBD-Ko (a Contextually Annotated Review Benchmark Dataset for Aspect-Based Sentiment Classification in Korean), a benchmark dataset that incorporates aspects and dual-tagged polarities to distinguish between aspect-specific and aspect-agnostic sentiment classification. The dataset consists of sentences annotated with specific aspects, aspect polarity, aspect-agnostic polarity, and the intensity of aspects. To address the issue of dual-tagged aspect polarities, we propose a novel approach employing a Siamese Network. Our experimental findings highlight the inherent difficulties in accurately predicting dual-polarities and underscore the significance of contextualized sentiment analysis models. The CARBD-Ko dataset serves as a valuable resource for future research endeavors in aspect-level sentiment classification.
Abstract (translated)
本文探讨了在预训练语言模型（PLMs）中，面向 aspect 的情感分类（ASBC）所面临的挑战，特别是上下文理解和虚构问题。为解决这些挑战，我们引入了 CARBD-Ko（一种关注于具有上下文注释的韩国 aspect 情感分类基准数据集），作为具有 aspect 和双重标记极性的基准数据集，用于区分面向特定和面向无关情感分类。数据集包括带有特定 aspects、aspect 极性、aspect-agnostic 极性和极值强度的句子注释。为了应对双重标记极性的问题，我们提出了一个新的采用 Siamese 网络的方法。我们的实验结果突出了准确预测双重极性的固有困难，并强调了上下文情感分析模型的必要性。CARBD-Ko 数据集成为未来研究在 aspect 级别情感分类方面的重要资源。
URL
https://arxiv.org/abs/2402.15046
