“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二十三届中文计算语言学大会中文抽象语义表示解析评测任务中提交的参赛系统。中文抽象语义表示(Chinese Abstract Meaning Representa-tion,CAMR)以一个单根可遍历的有向无环图表示中文句子的语义。本系统选择大语言模型作为解决方案。我们首先系统地评估了当下中文大语言模型在AMR解析任务上的性能,在此基础上基于图融合算法整合性能较高的大模型预测结果,最终得到预测的CAMR图。实验结果表明,1)现有大模型已经具备一定的少样本中文AMR解析能力;2)基于微调中文大模型的AMR解析系统能够取得相较以往最优系统更强的性能;3)图融合算法能够进一步增强基于大模型的CAMR解析系统的性能。”

“This paper introduces our systems submitted for the Chinese Vision-Language Understanding Evaluation task at the 23rd Chinese Computational Linguistics Conference.In this competition, we utilized X2-VLM and CCLM models to participate in various subtasks such as image-text retrieval, visual grounding, visual dialogue, and visual question answering. Additionally, we employed other models to assess performance on certain subtasks. We optimized our models and successfully applied them to these different tasks”

This paper describes an approach to preprocess SMS text for Machine Translation. As SMS text behaves differently from normal written text and to reduce the tremendous effort required to customize or adapt the language model of the traditional translation system to handle SMS text style, normalization is performed to moderate the irregularities in English SMS text using a noisy channel model. A mapping model is used to model the three major problems in SMS text. They are (1) substitution of word using non-standard acronym, (2) insertion of flavour word, and (3) omission of auxiliary verb and subject pronoun. Experiment results show that with normalization before translation, the rejection rate of our English-to-Chinese SMS translation for broadcasting purpose is reduced by 15.5%. We believe that the performance of normalization can be further improved with deeper linguistic processing.