A Two-stage Generative C hinese AMR Parsing Method Based on Large Language Models (2024)

Abstract: “The purpose of the CAMR task is to convert natural language into a formalized semantic representation in the form of a graph structure. Due to the complexity of the AMR graph structure, traditional AMR automatic parsing methods often require the design of complex models and strategies. Thanks to the powerful generative capabilities of LLMs, adopting an autore-gressive generative approach for AMR parsing has many advantages such as simple modeling and strong extensibility. To further explore the generative AMR automatic parsing technology based on LLMs, we design a two-stage AMR automatic parsing method based on LLMs in this CAMR evaluation. Specifically, we design two pipeline subtasks of alignment-aware node generation and relationship-aware node generation to reduce the difficulty of LLM understanding and generation. Additionally, to boost the system’s transferability, we incorporate a retrieval-augmented strategy during both training and inference phases. The experimental results show that the method we proposed has achieved promising results in this evaluation.”

## introduction semantic parsing is one of the fundamental tasks in the field of natural language processing (nlp). as an important formal method of semantic parsing, abstract meaning representation (amr) @xcite has attracted widespread attention from researchers in the nlp field. amr represents the semantics of a sentence in the form of a single-rooted directed acyclic graph (dag), where nodes are used to represent semantic concepts abstracted from the sentence, and edges are used to represent the semantic relations between concepts. thanks to the development of amr technology, researchers have also begun to try to integrate the semantic knowledge in amr graphs into application systems such as machine translation @xcite ), text summarization @xcite , and event extraction @xcite , in order to enhance the system's understanding of semantic knowledge and improve system performance. the data resources and automatic parsing technologies associated with amr primarily concentrate on english. @xcite considering the traits of chinese, establish the annotation standards and evaluation methods fitting for chinese amr (camr), significantly advancing the progress of chinese amr research. the existing camr automatic parsing technologies can be divided into three categories: transitionbased methods @xcite , graph-based methods @xcite , and autoregressive generative methods @xcite . the transition-based method considers the amr automatic parsing problem as a sequence-to-action conversion issue. this method usually relies on a complex transition system for action definition and feature extraction used for action prediction. the graph-based method treats the amr automatic parsing problem as a graph search issue, that is, searching for the highest scoring subgraph from a directed complete graph. due to the complexity of the amr automatic parsing task, the graph-based method usually requires the construction of multiple models such as concept recognition and relation recognition to achieve a complete parsing process. the autoregressive generative method considers the amr automatic parsing problem as a sequence-tosequence generation issue. compared with the other two methods, the autoregressive generative method has the advantages of simple modeling and easy implementation, and has become the mainstream method for solving amr automatic parsing problems. recently, thanks to the powerful generation capabilities of large language models (llms) @xcite , the autoregressive generative methods based on llms have also been adopted by more and more researchers and achieved competitive results @xcite . the existing autoregressive generative methods based on llms usually adopt a strategy of generating amr graphs in a single stage @xcite , such as directly generating linearized sequences of amr graphs or generating tuples within amr graphs. given the complexity of the amr graph structure, the difficulty of generating amr graphs in a single stage is significant, which constitutes a huge challenge for the generation ability of llms. in order to reduce the difficulty of generating amr graphs by llms, we decompose the process of generating amr graphs into two stages. the first stage is alignment-aware node generation, and the second stage is relation-aware tuple generation. in the first stage, we first group the nodes according to the alignment type between the nodes in the amr graph and the words in the sentence, and then let the llm generate different aligned type nodes in groups. in the second stage, we first group the tuples in the amr graph according to the relation type, and then let the llm generate tuples of different relation types in groups. considering that this shared task requires testing the system's ability to automatically parse ancient chinese sentences into amr graphs in a zero-shot scenario, we adopt a retrieval-augmented instruction tuning strategy to fine-tune the llm, with the aim of enhancing our system's transferability. ## method our method can be divided into three parts to descirbe: alignment-aware node generation, relation-aware tuple generation, and retrieval-augmented instruction tuning strategy. figure 1 shows the flowchart of our method. our system takes a sentence and the word information in the sentence as input, and after performing two pipeline tasks of alignment-aware node generation and relation-aware tuple generation, it obtains the entire amr graph as output. ## conclusion in this paper, we propose a two-stage generative camr parsing method based on llms. we decompose the camr task into two pipeline subtasks: alignment-aware node generation and relation-aware tuple generation to reduce the difficulty of understanding the camr task for llms. furthermore, we introduce retrieval-augmented strategy during training and inference to enhance the transfer learning capabilities of our system. on three test sets of the camr 2024 evaluation task, our system achieve promising results. through the analysis of experimental results, we find that our system still performs poorly on the ancient chinese test set, which inspires us to further enhance our system's camr autoparsing ability in low-resource scenarios through pre-training, data augmentation, and other techniques in future works.