"本文介绍了我们在第二十五届中国计算语言学大会(CCL 2025)中文叙实性推理评测(FIE2025)中荣获双赛道第一名和第二名的系统方案。针对中文叙实性推理任务中模型需要从谓词语义正确推断事件真实性的挑战,我们提出了层次化思维链(Hierarchical Chain-of-Thought, HCoT)推理框架,通过结构化的多级推理过程引导模型逐步识别关键谓词、分析其叙实性类型及其在否定、疑问等复杂语境下的叙实性变化。在非微调赛道中,我们通过集成多种强大的推理型大模型(如Deepseek-R1-671B、Deepseek-v3-671B、GPT-4o、Gemini-2.5-pro-0506等)的预测结果,并采用自适应投票策略,取得了0.9376的分数。在微调赛道上,我们构建了高质量的思维链指令数据集,发现专注于推理能力的基础模型(如DeepSeek-R1-Distill-Qwen-32B)经微调后在叙实性推理任务上优于同等规模甚至更大参数量的通用大模型(如Qwen2.5-72B-Instruct)。通过伪标签训练进一步优化,最终在官方评测中取得0.9396的最高正确率。实验结果表明,我们提出的层次化思维链结构与推理模型的结合在中文叙实性推理任务中具有显著优势,特别是在处理复杂语境和隐含语义的情况下。"

Service accounts, including organizations’ official accounts and mini-programs, provide various convenient services for users, and have become crucial components of a number of applications. Therefore, retrieving service accounts quickly and accurately is vital. However, this task suffers from the problem of limited human annotation, i.e., manually assessing account functionality and assigning ratings based on user experience is both labor-intensive and time-consuming. To this end, this paper proposes a novel approach, the Auxiliary task Boosted Multi-Task Learning method (AuxBoost-MTL). Specifically, the proposed method introduces multiple auxiliary tasks, which is able to utilized the log data from our application as supervision, and enhance the performance of the main task, service account retrieval. Furthermore, we introduce an Adaptive Hierarchical Fusion Module (AHF module) into our approach. This module is designed to adaptively perform hierarchical fusion of embeddings from auxiliary tasks into the main task, thereby enhancing the model efficacy. Experiments on two real-world industrial datasets demonstrate the effectiveness of our proposed approach.