“This paper presents our system submitted to the Chinese Frame Semantic Parsing evaluation task at the 24th China National Conference on Computational Linguistics (CCL2025). For the three subtasks of Frame Identification (FI), Argument Identification (AI), and Role Identification(RI), we utilized a larger Chinese pre-trained model, as the foundation and adopted specific optimization strategies for FI and RI subtasks. Specifically, we incorporated word segmentation structure information and updatable pre-trained target word embeddings in the FI subtask, and explored the use of Focal Loss combined with target word embeddings and word segmentation structure information in the RI subtask. Furthermore, a voting mechanism was employed in both the FI and RI subtasks to enhance performance. Our system ultimately achieved first place on the TestA and second place on the TestB.”

“We participate in the open track of the Chinese frame semantic parsing (CFSP) task, i.e., CCL24Eval Task 1, and our submission ranks first. FSP is an important task in Natural Language Processing, aiming to extract the frame semantic structures from sentences, which can be divided into three subtasks, e.g., Frame Identification (FI), Argument Identification (AI), and Role Identification (RI). In this paper, we use the LLM Gemini 1.0 to evaluate the three subtasks of CFSP, and present the techniques and strategies we employed to enhance subtasks performance. For FI, we leverage mapping and similarity strategies to minimize the candidate frames for each target word, which can reduce the complexity of the LLM in identifying the appropriate frame. For AI and RI subtasks, we utilize the results from small models as auxiliary information and apply data augmentation, self-training, and model ensemble techniques on these small models to further enhance the performance of subtasks.”

“本文介绍了我们在第二十二届中国计算语言学大会汉语框架语义解析评测中提交的参赛系统。框架语义解析是自然语言处理领域中重要的任务,其目标是从句子中提取框架语义结构。本次评测任务针对汉语框架语义的三个子任务(框架识别、论元范围识别和论元角色识别)使用不同的端到端框架进行解析,并利用数据增强和投票方法进一步提高预测的精度,最终,在A榜测试集上取得第二名,B榜测试集上取得第三名。”

Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks. However, they tend to generate generic/dull responses, leading to unsatisfying dialogue experience. We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus. The objective function used in standard sequence-to-sequence models will be dominated by loss terms with generic patterns. Inspired by this observation, we introduce a statistical re-weighting method that assigns different weights for the multiple responses of the same query, and trains the common neural generation model with the weights. Experimental results on a large Chinese dialogue corpus show that our method improves the acceptance rate of generated responses compared with several baseline models and significantly reduces the number of generated generic responses.