  Joint extraction of entities and relations aims to detect entity pairs along
with their relations using a single model. Prior work typically solves this
task in the extract-then-classify or unified labeling manner. However, these
methods either suffer from the redundant entity pairs, or ignore the important
inner structure in the process of extracting entities and relations. To address
these limitations, in this paper, we first decompose the joint extraction task
into two interrelated subtasks, namely HE extraction and TER extraction. The
former subtask is to distinguish all head-entities that may be involved with
target relations, and the latter is to identify corresponding tail-entities and
relations for each extracted head-entity. Next, these two subtasks are further
deconstructed into several sequence labeling problems based on our proposed
span-based tagging scheme, which are conveniently solved by a hierarchical
boundary tagger and a multi-span decoding algorithm. Owing to the reasonable
decomposition strategy, our model can fully capture the semantic
interdependency between different steps, as well as reduce noise from
irrelevant entity pairs. Experimental results show that our method outperforms
previous work by 5.2%, 5.9% and 21.5% (F1 score), achieving a new
state-of-the-art on three public datasets
