Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale autolabeled data. However, its evaluation has long been a problem: previous works either take costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled datawhich, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset. This problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE. To evaluate DS-RE models more credibly, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. The experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared with previous methods. We hope that both our manual test sets and observations can help advance future DS-RE research.
Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale autolabeled data. However, its evaluation has long been a problem: previous works either take costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled datawhich, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset. This problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE. To evaluate DS-RE models more credibly, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. The experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared with previous methods. We hope that both our manual test sets and observations can help advance future DS-RE research.
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