ROAST：面向ABSA的评论级观点、情感、目标联合检测
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA
AI总结：
本研究提出评论级ABSA任务ROAST，通过扩展多语言及低资源数据集，实现评论级观点、情感、目标联合检测，弥合句子级与文本级ABSA差距。
摘要：
基于方面的情感分析（ABSA）由于通过SemEval研讨会和Germeval组织的、跨越多种语言和领域的各种共享任务而经历了巨大的扩展和多样化。尽管如此，仍有一些缺点需要解决，例如缺乏低资源语言评估以及对句子级分析的强调。为了在完整评论的背景下全面评估ABSA技术，本研究提出了一项新任务，即评论级观点、情感、目标（ROAST）。ROAST旨在通过识别评论级别的每个ABSA组成部分来弥合句子级和文本级ABSA之间的差距。我们扩展了可用数据集以实现ROAST，通过纳入低资源语言、多种语言和各种主题来解决先前研究中指出的缺点。通过这项工作，ABSA研究将能够覆盖更多领域，并对该任务及其在各种语言和领域中的实际应用有更深入的理解（https://github.com/RiTUAL-UH/ROAST-ABSA）。
英文摘要：
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 (https://github.com/RiTUAL-UH/ROAST-ABSA).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
