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This research aims to develop BERT-based models for improving relation extraction and semantic role labeling.

April 10, 2019Open Access

Simple BERT Models for Relation Extraction and Semantic Role Labeling

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This research aims to develop BERT-based models for improving relation extraction and semantic role labeling.
Conducted extensive experiments using simple BERT models on relevant datasets.
Evaluated performance without external features, focusing solely on BERT capabilities.
Compared results against existing state-of-the-art models.
Achieved state-of-the-art performance in both relation extraction and semantic role labeling tasks.
Demonstrated that simple BERT models can outperform complex systems by effectively leveraging internal features.

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Randomized trial shows high performance in relation extraction and semantic role labeling, suggesting BERT's effectiveness in natural language tasks.

Shi et al. (2019) studied this question.

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Randomized trial shows high performance in relation extraction and semantic role labeling, suggesting BERT's effectiveness in natural language tasks.