Dependency Parsing with the Structuralized Prompt Template
This addresses dependency parsing in NLP with a novel approach that is adaptable across languages and training environments, though it appears incremental as it builds on existing pre-trained models.
The authors tackled dependency parsing by proposing a method that uses only an encoder model with text-to-text training and a structured prompt template to capture dependency tree structure, achieving outstanding performance compared to traditional models.
Dependency parsing is a fundamental task in natural language processing (NLP), aiming to identify syntactic dependencies and construct a syntactic tree for a given sentence. Traditional dependency parsing models typically construct embeddings and utilize additional layers for prediction. We propose a novel dependency parsing method that relies solely on an encoder model with a text-to-text training approach. To facilitate this, we introduce a structured prompt template that effectively captures the structural information of dependency trees. Our experimental results demonstrate that the proposed method achieves outstanding performance compared to traditional models, despite relying solely on a pre-trained model. Furthermore, this method is highly adaptable to various pre-trained models across different target languages and training environments, allowing easy integration of task-specific features.