Aspect-based Sentiment Analysis (ABSA) helps to explain customers' opinions towards products and services. In the past, ABSA models were discriminative, but more recently generative models have been used to generate aspects and polarities directly from text. In contrast, discriminative models commonly first select aspects from the text, and then classify the aspect's polarity. Previous results showed that generative models outperform discriminative models on several English ABSA datasets. Here, we evaluate and contrast two state-of-the-art discriminative and generative models in several settings: cross-lingual, cross-domain, and cross-lingual and domain, to understand generalizability in settings other than English mono-lingual in-domain. Our more thorough evaluation shows that, contrary to previous studies, discriminative models can still outperform generative models in almost all settings.

翻译：基于方面的情感分析（ABSA）有助于解释顾客对产品和服务的意见。过去，ABSA模型多为判别式模型，但近来生成式模型已被用于直接从文本中生成方面和极性。相比之下，判别式模型通常先从文本中选择方面，再对方面的极性进行分类。以往研究结果显示，在多个英文ABSA数据集上，生成式模型优于判别式模型。本研究在跨语言、跨领域、以及跨语言与跨领域等多种场景下，评估并对比了两类最先进的判别式与生成式模型，以理解它们在英语单语域内环境之外的可推广性。我们的更全面评估表明，与以往研究相反，判别式模型在几乎全部场景下仍可优于生成式模型。