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Span-based joint extraction simultaneously conducts named entity recognition(NER) and relation extraction (RE) in text span form. However, since previousspan-based models rely on span-level classifications, they cannot benefit fromtoken-level label information, which has been proven advantageous for the task.In this paper, we propose a Sequence Tagging augmented Span-based Network(STSN), a span-based joint model that can make use of token-level labelinformation. In STSN, we construct a core neural architecture by deep stackingmultiple attention layers, each of which consists of three basic attentionunits. On the one hand, the core architecture enables our model to learntoken-level label information via the sequence tagging mechanism and then usesthe information in the span-based joint extraction; on the other hand, itestablishes a bi-directional information interaction between NER and RE.Experimental results on three benchmark datasets show that STSN consistentlyoutperforms the strongest baselines in terms of F1, creating newstate-of-the-art results.

Span-based joint entity and relation extraction augmented with sequence tagging mechanism