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 behind human performance . In this paper , we revisit two aspects of RE models that are not thoroughly studied , namely entity representation and NA instance prediction . Our improved baseline model, incorporated with entity representations with type markersand confidence-based classification for enhanced NA instance detection , achieves an F1 of 75.0\% on TACRED, significantly outperforms previous SOTA methods  .
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 .
R
behind human performance
from perfection
R
paper
report
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aspects of RE models that are not thoroughly studied
problems that affect the performance of existing RE models
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NA instance prediction
noisy or ill-defined labels
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type markersand confidence-based classification for enhanced NA instance detection
typed markers
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75.0\%
74.6
A
. 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