End-to-end Dependency Parsing via Auto-regressive Large Language Models

This paper presents a straightforward application of Large Language Models (LLMs) for Dependency Parsing. The parsing process is approached as a sequence-to-sequence task, where a language model takes a sentence as input and generates a bracketed form, allowing for the deterministic derivation of the dependency graph. The experimental evaluation explores the feasibility of utilizing LLMs for this purpose, while also assessing the process’s sustainability with modest parameter sizes (training on a single GPU with limited resources) and investigating the impact of incorporating multilingual data during training. The results demonstrate that an end-to-end dependency parsing process can indeed be formulated using a task-agnostic architecture.