Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation

Understand

Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU).

• The recent advent of end-to-end neural models, self-supervised via language modeling (LM), and their success on a wide range of LU tasks, however, questions this belief.
• In this work, we empirically investigate the usefulness of supervised parsing for semantic LU in the context of LM-pretrained transformer networks.
• Relying on the established fine-tuning paradigm, we first couple a pretrained transformer with a biaffine parsing head, aiming to infuse explicit syntactic knowledge from Universal Dependencies treebanks into the transformer.