Aspect-based sentiment analysis is a long-standing research interest in the
field of opinion mining, and in recent years, researchers have gradually
shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA
tasks. However, the datasets currently used in the research are limited to
individual elements of specific tasks, usually focusing on in-domain settings,
ignoring implicit aspects and opinions, and with a small data scale. To address
these issues, we propose a large-scale Multi-Element Multi-Domain dataset
(MEMD) that covers the four elements across five domains, including nearly
20,000 review sentences and 30,000 quadruples annotated with explicit and
implicit aspects and opinions for ABSA research. Meanwhile, we evaluate
generative and non-generative baselines on multiple ABSA subtasks under the
open domain setting, and the results show that open domain ABSA as well as
mining implicit aspects and opinions remain ongoing challenges to be addressed.
The datasets are publicly released at https://github.com/NUSTM/MEMD-ABSA.

提出了一个大规模的多元素多领域数据集（MEMD），覆盖了包括近 20,000 个评论句子和 30,000 个四元组在内的四个元素的五个领域，评估了开放域 ABSA 的生成和非生成基线，并发现开放域 ABSA 和挖掘隐含方面和观点仍然是需要解决的问题。