Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Haoyu Wang, Ming Tan, Mo Yu, Shiyu Chang, Dakuo Wang, Kun Xu, Xiaoxiao Guo, Saloni Potdar · 2019
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.1 * Equal contributions from the corresponding authors: {wanghaoy,mingtan,yum}@us.ibm.com.Part of