Aspect-Based Sentiment Analysis (ABSA) has experienced tremendous expansion
and diversity due to various shared tasks spanning several languages and fields
and organized via SemEval workshops and Germeval. Nonetheless, a few
shortcomings still need to be addressed, such as the lack of low-resource
language evaluations and the emphasis on sentence-level analysis. To thoroughly
assess ABSA techniques in the context of complete reviews, this research
presents a novel task, Review-Level Opinion Aspect Sentiment Target (ROAST).
ROAST seeks to close the gap between sentence-level and text-level ABSA by
identifying every ABSA constituent at the review level. We extend the available
datasets to enable ROAST, addressing the drawbacks noted in previous research
by incorporating low-resource languages, numerous languages, and a variety of
topics. Through this effort, ABSA research will be able to cover more ground
and get a deeper comprehension of the task and its practical application in a
variety of languages and domains (this https URL).

该研究通过引入 ROAST 任务，致力于解决 ABSA 技术在完整评论文本中的评估问题，包括低资源语言的评估、句子级和文本级 ABSA 之间的差距，并通过扩展可用数据集以处理低资源语言、多种语言和各种主题，从而对 ABSA 技术进行全面评估，提高对任务及其在不同语言和领域中的实际应用的理解。

ROAST：评论级别观点方面情感目标联合检测

ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection

Fine-tuning pre-trained language models (LMs) has become the de facto
standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to
robustness issues, such as adversarial robustness and model calibration.
Several perspectives of robustness for LMs have been studied independently, but
lacking a unified consideration in multiple perspectives. In this paper, we
propose Robustifying LMs via Adversarial perturbation with Selective Training
(RoAST), a simple yet effective fine-tuning technique to enhance the
multi-perspective robustness of LMs in a unified way. RoAST effectively
incorporates two important sources for the model robustness, robustness on the
perturbed inputs and generalizable knowledge in pre-trained LMs. To be
specific, RoAST introduces adversarial perturbation during fine-tuning while
the model parameters are selectively updated upon their relative importance to
minimize unnecessary deviation. Under a unified evaluation of fine-tuned LMs by
incorporating four representative perspectives of model robustness, we
demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning
methods on six different types of LMs, which indicates its usefulness in
practice.

RoAST 是一种简单而有效的微调技术，通过在微调期间引入对抗性扰动，并且在相对重要性上选择性地更新模型参数，以提升语言模型的多角度鲁棒性。通过统一评估微调的语言模型在四个代表性的鲁棒性角度下，我们证明了 RoAST 相对于最先进的微调方法在六种不同类型的语言模型上的有效性，表明其在实践中的有用性。