Efficient long-distance relation extraction with DG-SpanBERT
This work addresses relation extraction for natural language processing applications, representing an incremental improvement over existing methods.
The paper tackled the problem of extracting long-distance relations between entities in text by proposing DG-SpanBERT, a model combining SpanBERT with a graph convolutional network on dependency trees, which achieved state-of-the-art performance on the TACRED dataset.
In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBERT) that extracts semantic features from a raw sentence using the pre-trained language model SpanBERT and a graph convolutional network to pool latent features. Our DG-SpanBERT model inherits the advantage of SpanBERT on learning rich lexical features from large-scale corpus. It also has the ability to capture long-range relations between entities due to the usage of GCN on dependency tree. The experimental results show that our model outperforms other existing dependency-based and sequence-based models and achieves a state-of-the-art performance on the TACRED dataset.