  Many joint entity relation extraction models setup two separated label spaces
for the two sub-tasks (i.e., entity detection and relation classification). We
argue that this setting may hinder the information interaction between entities
and relations. In this work, we propose to eliminate the different treatment on
the two sub-tasks' label spaces. The input of our model is a table containing
all word pairs from a sentence. Entities and relations are represented by
squares and rectangles in the table. We apply a unified classifier to predict
each cell's label, which unifies the learning of two sub-tasks. For testing, an
effective (yet fast) approximate decoder is proposed for finding squares and
rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC)
show that, using only half the number of parameters, our model achieves
competitive accuracy with the best extractor, and is faster.
