Semantic Role Labeling with BERT-Based Transformers
Transformers have made more progress in the past few years than NLP in the past generation. Standard NLU approaches first learn syntactical and lexical features to explain the structure of a sentence. The former NLP models would be trained to understand the basic syntax of language before running Semantic Role Labeling (SRL).
Shi and Lin (2019) start their paper by asking if preliminary syntactic and lexical training can be skipped. Can a BERT-based model perform SRL without going through those classical training phases? The answer is yes!
Shi and Lin (2019) suggest that SRL can be considered as sequence labeling and provide a standardized input format. Their BERT-based model produced surprisingly good results.
In this chapter, we will use a pretrained BERT-based model provided by the Allen Institute for AI based on the Shi and Lin (2019) paper. Shi and Lin took SRL to the next level by dropping syntactic and lexical...
