Abstract
The state-of-the-art solutions for extracting
multiple entity-relations from an input paragraph always require a multiple-pass encoding
on the input. This paper proposes a new solution that can complete the multiple entityrelations extraction task with only one-pass
encoding on the input corpus, and achieve a
new state-of-the-art accuracy performance,
as demonstrated in the ACE 2005 benchmark.
Our solution is built on top of the pre-trained
self-attentive models (Transformer). Since our
method uses a single-pass to compute all relations at once, it scales to larger datasets easily;
which makes it more usable in real-world applications