Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. Many efforts have been devoted to this problem, while the best performing methods are still far from perfection . In this report , we revisit two problems that affect the performance of existing RE models, namely entity representation and noisy or ill-defined labels. Our improved baseline model, incorporated with entity representations with typed markers, achieves an F1 of 74.6  on TACRED, significantly outperforms previous SOTA methods. Furthermore, the presented new baseline achieves an F1 of 91.1\% on the refined Re-TACRED dataset, demonstrating that the pre-trained language models achieve unexpectedly high performance on this task. We release our code to the community for future research.
Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. Many efforts have been devoted to this problem, while the best performing methods are still far from perfect . In this paper , we revisit two problems that affect the performance of existing RE models, namely entity representation and noisy or ill-defined labels. Our improved baseline model, incorporated with entity representations with typed markers, achieves an F1 of 74.6 \% on TACRED, significantly outperforms previous SOTA methods. Furthermore, the presented new baseline achieves an F1 of 91.1\% on the refined Re-TACRED dataset, demonstrating that the pre-trained language models achieve unexpectedly high performance on this task. We release our code to the community for future research.
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