CCL 23-Eval 任务3系统报告:苏州大学 CFSP 系统(System Report for CCL 23-Eval Task3: SUDA CFSP System) (2023)

Abstract: “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。”

## introduction with the growing maturity of morphological analysis and syntactic analysis techniques, natural language processing in general has advanced to semantic analysis level. sentence-level meaning parsing, to be more specific, has already occupied the core position of semantic analysis research. to address the lack of whole-sentence semantic representation and the domain-dependent problem of sentence semantic annotation, @xcite proposed a domain-independent whole-sentence semantic representation method called abstract meaning representation (amr) that can abstract the meaning of a sentence with a single-rooted, acyclic and directed graph and predicts the semantic structure of the targeted sentence. there have been large-scaled corpora constructed for amr and two international conferences held for amr semantic parsing evaluation tasks. the latest one was conll 2020, where there have been five languages in cross-lingual track including chinese. and yet parsing chinese via amr was not flawless given that chinese mandarin differs a lot from english in terms of syntax and semantics. @xcite therefore introduced several major changes into chinese abstract meaning representation (chinese amr, camr) so as to better parse chinese. and similar to amr, the corpus of camr has also begun to take shape and played an important role in the stage of conll 2020. and some extra semantic role labels as well to better distinguish characteristics in chinese. the evaluation task at conll 2020, however, failed to leverage the alignment of concept and relation. therefore, in our former camrp 2022 evaluation task, we adopted the newly-designed metric named align-smatch, which contains the alignment of concept and relation, aiming to better evaluate the performance of automatic parsing. camrp 2023 is a follow-up and extension of camrp 2022, with key difference including the addition of a blind test set with 2,000 interrogative sentences. ## conclusion and future work this paper introduced the overview of the third chinese abstract meaning representation parsing evaluation in ccl 2023. camrp 2023 uses the novel metric align-smatch to better evaluate the parsing performance of each participating parsing system. there have been six teams in total submitted their c c l 2 0 2 3 in our future endeavors, we are committed to dedicating extensive efforts to advance chinese amr parsing. this includes hosting evaluation tasks to facilitate the assessment and benchmarking of parsing models. additionally, we aim to construct and refine parsing models that are specifically tailored to the intricacies of chinese amr, ultimately driving forward the field of semantic analysis. by focusing on relation prediction and alignment, we aim to overcome the current challenges and enhance the overall performance and understanding of chinese amr parsing. through continuous research, collaboration, and innovation, we aspire to contribute to the development of robust and accurate parsing models, pushing the boundaries of semantic analysis further.