English(EN) Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction

新架构提升低秩模型中的时序关系抽取能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种名为卷积瓶颈交互（CBI）的新架构，以在参数高效微调场景中改进时序关系抽取。该方法通过使用深度卷积增强事件表示和使用逐元素乘法进行事件-事件交互，来解决低秩瓶颈中的信息流限制。CBI在多个数据集和骨干模型上都显示出显著的性能提升，同时计算成本增加极少。 AI

影响在参数高效微调的时序关系抽取任务中提升了性能。

排序理由该集群包含一篇详细介绍特定NLP任务新架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Wei Sun, Tingyu Qu, Jesse Davis, Marie-Francine Moens · 2026-09-10 04:00

面向时序关系抽取的低秩瓶颈中的事件交互

arXiv:2609.06731v1 Announce Type: cross Abstract: Temporal relation extraction determines whether an event occurs before, after, or simultaneously with another event, and therefore relies on accurately modeling how the two events interact. Mainstream systems achieve this by conca…