分类:Combining Distant and Direct Supervision for Neural Relation Extraction

Iz Beltagy, Kyle Lo, Waleed Ammar. Combining Distant and Direct Supervision for Neural Relation Extraction. NAACL-HLT 2019

Abstract

In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model's ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.

总结和评论

这篇文章对引用动机做了识别，也提供了一个用来训练引文动机识别模型的数据库。

概念地图