  Aspect Sentiment Triplet Extraction (ASTE) is widely used in various
applications. However, existing ASTE datasets are limited in their ability to
represent real-world scenarios, hindering the advancement of research in this
area. In this paper, we introduce a new dataset, named DMASTE, which is
manually annotated to better fit real-world scenarios by providing more diverse
and realistic reviews for the task. The dataset includes various lengths,
diverse expressions, more aspect types, and more domains than existing
datasets. We conduct extensive experiments on DMASTE in multiple settings to
evaluate previous ASTE approaches. Empirical results demonstrate that DMASTE is
a more challenging ASTE dataset. Further analyses of in-domain and cross-domain
settings provide promising directions for future research. Our code and dataset
are available at https://github.com/NJUNLP/DMASTE.
