  Extracting relational triples from unstructured text is crucial for
large-scale knowledge graph construction. However, few existing works excel in
solving the overlapping triple problem where multiple relational triples in the
same sentence share the same entities. In this work, we introduce a fresh
perspective to revisit the relational triple extraction task and propose a
novel cascade binary tagging framework (CasRel) derived from a principled
problem formulation. Instead of treating relations as discrete labels as in
previous works, our new framework models relations as functions that map
subjects to objects in a sentence, which naturally handles the overlapping
problem. Experiments show that the CasRel framework already outperforms
state-of-the-art methods even when its encoder module uses a randomly
initialized BERT encoder, showing the power of the new tagging framework. It
enjoys further performance boost when employing a pre-trained BERT encoder,
outperforming the strongest baseline by 17.5 and 30.2 absolute gain in F1-score
on two public datasets NYT and WebNLG, respectively. In-depth analysis on
different scenarios of overlapping triples shows that the method delivers
consistent performance gain across all these scenarios. The source code and
data are released online.
