论文标题
交叉缝制文本和知识图编码器，用于远距离监督的关系提取
Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction
论文作者
论文摘要
用于远程监督关系提取的双重编码器架构旨在利用文本和知识图（kg）中发现的互补信息。但是，当前的体系结构遭受了两个缺点。他们要么根本不允许在文本编码器和kg编码器之间进行任何共享，要么在关注kg到文本的模型中，只能在一个方向上共享信息。在这里，我们介绍了交叉缝制双编码器，该编码器允许通过跨缝机制在文本编码器和kg编码器之间进行完全相互作用。交叉缝制机制允许在任何一层的两个编码器之间共享和更新表示形式，共享的量是通过基于跨注意的门动态控制的。从两个不同领域的两个关系提取基准的实验结果表明，两个编码器之间的完全相互作用可实现强烈的改进。
Bi-encoder architectures for distantly-supervised relation extraction are designed to make use of the complementary information found in text and knowledge graphs (KG). However, current architectures suffer from two drawbacks. They either do not allow any sharing between the text encoder and the KG encoder at all, or, in case of models with KG-to-text attention, only share information in one direction. Here, we introduce cross-stitch bi-encoders, which allow full interaction between the text encoder and the KG encoder via a cross-stitch mechanism. The cross-stitch mechanism allows sharing and updating representations between the two encoders at any layer, with the amount of sharing being dynamically controlled via cross-attention-based gates. Experimental results on two relation extraction benchmarks from two different domains show that enabling full interaction between the two encoders yields strong improvements.
