Extracting relational triples from texts is a fundamental task in knowledge
graph construction. The popular way of existing methods is to jointly extract
entities and relations using a single model, which often suffers from the
overlapping triple problem. That is, there are multiple relational triples that
share the same entities within one sentence. In this work, we propose an
effective cascade dual-decoder approach to extract overlapping relational
triples, which includes a text-specific relation decoder and a
relation-corresponded entity decoder. Our approach is straightforward: the
text-specific relation decoder detects relations from a sentence according to
its text semantics and treats them as extra features to guide the entity
extraction; for each extracted relation, which is with trainable embedding, the
relation-corresponded entity decoder detects the corresponding head and tail
entities using a span-based tagging scheme. In this way, the overlapping triple
problem is tackled naturally. Experiments on two public datasets demonstrate
that our proposed approach outperforms state-of-the-art methods and achieves
better F1 scores under the strict evaluation metric. Our implementation is
available at this https URL

本文提出了一种有效的两级解码器方法来从文本中提取重叠的关系三元组，该方法通过具有可训练嵌入的关系相关实体解码器和特定于文本语义的关系解码器来解决重叠三元组问题，并在两个公共数据集上取得了优异的性能。