The abstract inference capability of the Language Model plays a pivotal role in boosting its generalization and reasoning prowess in Natural Language Inference (NLI). Entailment graphs are crafted precisely for this purpose, focusing on learning entailment relations among predicates. Yet, prevailing approaches overlook the *polysemy* and *hierarchical nature of concepts* during entity conceptualization. This oversight disregards how arguments might entail differently across various concept levels, thereby missing potential entailment connections. To tackle this hurdle, we introduce the *concept pyramid* and propose the HiCon-EG (Hierarchical Conceptual Entailment Graph) framework, which organizes arguments hierarchically, delving into entailment relations at diverse concept levels. By learning entailment relationships at different concept levels, the model is guided to better understand concepts so as to improve its abstract inference capabilities. Our method enhances scalability and efficiency in acquiring common-sense knowledge through leveraging statistical language distribution instead of manual labeling, Experimental results show that entailment relations derived from HiCon-EG significantly bolster abstract detection tasks. Our code is available at https://github.com/SXUCFN/HiCon-EG

“零形式填充是在篇章上下文中为给定句子中的隐式框架语义角色找到相应的填充内容。传统的零形式填充方法采用pipeline模型,容易造成错误传播,并且忽略了显式语义角色及其填充内容的重要性。针对上述问题,本文提出了一种端到端的零形式填充方法,该方法结合汉语框架网信息构建出框架语义场景图并利用GAT对其建模,得到融合了显式框架元素信息的候选填充项表示,增强了模型对句中隐式语义成分的识别能力。在汉语零形式填充数据集上的实验表明,本文提出的模型相较于基于Bert的基线模型F1值提升了9.16%,证明了本文提出方法的有效性。”

“汉语框架语义解析评测任务致力于提升机器模型理解细粒度语义信息的能力。该评测数据集包括20000条标注的框架语义解析例句和近700个框架信息。评测任务分为框架识别、论元范围识别和论元角色识别三个子任务,最终成绩根据这三个任务的得分综合计算。本次评测受到工业界和学术界的广泛关注,共有55支队伍报名参赛,其中12支队伍提交了结果,我们选取5支队伍的模型进行结果复现,最终来自四川的李作恒以71.49的分数排名第一。该任务的更多信息,包括系统提交、评测结果以及数据资源,可从CCL-2023汉语框架语义解析评测任务网址1查看。”