"企业新闻事件抽取是支撑企业动态分析与产业决策的关键技术。企业新闻事件抽取具有文本篇幅较长,内容多元化的特点,面临多事件抽取和论元分散等核心挑战。大语言模型(Large Language Model,LLM)虽然具有强大的长距离依赖建模和语义关联能力,但通用大语言模型难以满足企业级应用对专业性与资源效率的需求。本文提出了融合MoE的多任务学习企业新闻事件抽取模型(MoE-Enhanced Multi-Task Learning for Corporate News Event Extraction,MoE-ML-CNEE)。通过构建统一微调数据集与多任务联合训练范式,将事件检测与论元抽取构建为结构化语言模板,增强模型全局建模能力。设计MoELoRA模块,利用动态路由机制实现多专家网络在低秩空间的知识共享与特征解耦,进一步提升模型事件抽取性能。实验表明,MoE-ML-CNEE模型在ChiFinAnn和DuEE-fin公共数据集和自建企业新闻数据集的事件检测、事件论元抽取结果均优于现有基线模型。"

Document-level Relation Extraction (DocRE) aims to extract relations from documents. Compared with sentence-level relation extraction, it is necessary to extract long-distance dependencies. Existing methods enhance the output of trained DocRE models either by learning logical rules or by extracting rules from annotated data and then injecting them into the model. However, these approaches can result in suboptimal performance due to incorrect rule set constraints. To mitigate this issue, we propose Context-aware differentiable rule learning or CaDRL for short, a novel differentiable rule-based framework that learns the doc-specific logical rule to avoid generating suboptimal constraints. Specifically, we utilize Transformer-based relation attention to encode document and relation information, thereby learning the contextual information of the relation. We employ a sequence-generated differentiable rule decoder to generate relational probabilistic logic rules at each reasoning step. We also introduce a parameter sharing training mechanism in CaDRL to reconcile the DocRE model and the rule learning module. Extensive experimental results on three DocRE datasets demonstrate that CaDRL outperforms existing rule-based frameworks, significantly improving DocRE performance and making predictions more interpretable and logical.

“尽管现有语言模型在自然语言处理任务上表现出色,但在深层次语义理解和常识推理方面仍有提升空间。本研究通过测试模型在儿童故事常识推理与寓意理解数据集(CRMUS)上的性能,探究如何增强模型在复杂任务中的能力。在本次任务的赛道二中,本研究使用多个7B以内的开源大模型(如Qwen、InternLM等)进行零样本推理,并选择表现最优的模型基于LoRA进行指令微调来提高其表现。除此之外,本研究还对数据集进行了分析与增强。研究结果显示,通过设计有效的指令格式和调整LoRA微调参数,模型在常识推理和寓意理解上的准确率显著提高。最终在本次任务的赛道二中取得第一名的成绩,该任务的评价指标Acc值为74.38,达到了较为先进的水准。”