新闻人物言论的抽取对理解社会动态、分析公众意见、辅助决策制定至关重要。本文首先针对新闻场景定义了人物言论事件框架，然后提出了一种基于联合层级问答的新闻人物言论抽取方法。该方法改变原有的针对起始位置进行预测的指针标注方式和序列标注，采用联合层级标注，以提高模型在面对复杂人物名称时的识别能力，并利用多轮问答的特性，准确匹配人物名称和触发词，以解决复杂人物言论抽取中的多任务问题。为进一步提升模型训练效果，引入了额外位置向量和交替双向训练策略。实验结果表明，本文方法在（人物，触发词）抽取、言论抽取以及（人物、触发词、言论）三元组抽取任务中均取得了较好的结果。

The extraction of remarks made by news figures is crucial for understanding social dynamics, analyzing public opinion, and assisting decision-making. First，we defined an event framework for remarks in news scenarios. Then we proposed a novel method for extracting remarks made by news figures based on joint hierarchical question-answering. Departing from conventional pointer annotation and sequence labeling that predict starting positions, our method adopts joint hierarchical annotation to improve the model's ability to identify complex figure names. It also utilizes the characteristics of multi-round question-answering to accurately match figure names and trigger words, addressing the multitasking problem in extracting remarks made by complex figures. To further enhance the effectiveness of model training, this paper introduces extra positional embedding and Bi-Directional training frame. Experimental results show that the proposed method achieves good results in extracting (figure, trigger word) pairs, extracting remarks, and extracting (figure, trigger word, remark) triplets.