Computer Science > Computation and Language
[Submitted on 30 Oct 2018 (v1), last revised 6 Apr 2019 (this version, v2)]
Title:Combining Distant and Direct Supervision for Neural Relation Extraction
View PDFAbstract: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.
Submission history
From: Iz Beltagy [view email][v1] Tue, 30 Oct 2018 18:31:16 UTC (161 KB)
[v2] Sat, 6 Apr 2019 22:07:38 UTC (172 KB)
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