Introduction: Semantic Role Labeling with BERT-Based Transformers

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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 a language’s basic syntax before running semantic role labeling (SRL).

Shi and Lin (2019) started their paper “Simple BERT Models for Relation Extraction and Semantic Role Labeling”The paper can be accessed at: 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) suggested that SRL can be considered sequence labeling and provide a standardized input format. Their BERT-based model produced surprisingly good results.

Chapter overview

This chapter 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 training. We will see how this was achieved.

We will begin by defining SRL and the standardization of the sequence labeling input formats. We will then get started with the resources provided by the Allen Institute for AI. Next, we will run SRL tasks in a Jupyter notebook and use online resources to understand the results.

Finally, we will challenge the BERT-based model by running SRL samples. The first samples will show how SRL works. Then, we will run some more difficult samples. We will progressively push the BERT-based model to the limits of SRL. Finding the limits of a model is the best way to ensure that real-life implementations of transformer models remain realistic and pragmatic.

This chapter covers the following topics:

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