Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
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 entity-relations 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.
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
Chen-chia Chang, Wan-hsuan Lin, et al.
ICML 2025
Gang Liu, Michael Sun, et al.
ICLR 2025
Daniel Karl I. Weidele, Hendrik Strobelt, et al.
SysML 2019