Transformers have substantially enhanced the accuracy and efficiency of syntactic parsing by effectively capturing long-range dependencies and contextual information. ...
Transformers have substantially enhanced the accuracy and efficiency of syntactic parsing by effectively capturing long-range dependencies and contextual information. Leveraging pre-trained language models, fine-tuning, joint modeling, and graph-based parsing techniques, we can fully exploit the potential of transformers for this task. Nevertheless, it's imperative to address potential limitations such as data bias, computational cost, and interpretability. Moreover, ethical considerations pertaining to privacy, fairness, and accountability must be meticulously considered when developing and deploying transformer-based syntactic parsing models.