Control Flow Operators in PyTorch
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
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.
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010