Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose TEXT2EVENT, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.

Inflammatory responses play an important role in the pathogenesis of adverse ventricular remodeling after myocardial infarction (MI). We previously demonstrated that interleukin (IL)-17A plays a pathogenic role in myocardial ischemia/reperfusion injury and viral myocarditis. However, the role of IL-17A in post-MI remodeling and the related mechanisms have not been fully elucidated. Acute MI was induced by permanent ligation of the left anterior descending coronary artery in C57BL/6 mice. Repletion of IL-17A significantly aggravated both early- and late-phase ventricular remodeling, as demonstrated by increased infarct size, deteriorated cardiac function, increased myocardial fibrosis, and cardiomyocyte apoptosis. By contrast, genetic IL-17A deficiency had the opposite effect. Additional studies in vitro indicated that IL-17A induces neonatal cardiomyocyte (from C57BL/6 mice) apoptosis through the activation of p38, p53 phosphorylation, and Bax redistribution. These data demonstrate that IL-17A induces cardiomyocyte apoptosis through the p38 mitogen-activated protein kinase (MAPK)-p53-Bax signaling pathway and promotes both early- and late-phase post-MI ventricular remodeling. IL-17A might be an important target in preventing heart failure after MI. Key message: We demonstrated that IL-17A plays a pathogenic role both in the early and late stages of post-MI remodeling. IL-17A induces murine cardiomyocyte apoptosis. IL-17A induces murine cardiomyocyte apoptosis through the p38 MAPK-p53-Bax signaling pathway.

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases 1 .