1 code implementation • EMNLP (Louhi) 2020 • Andreas Grivas, Beatrice Alex, Claire Grover, Richard Tobin, William Whiteley
Our analysis finds that our rule-based system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset.
1 code implementation • 16 Oct 2023 • Andreas Grivas, Antonio Vergari, Adam Lopez
We then show that they can be prevented in practice by introducing a Discrete Fourier Transform (DFT) output layer, which guarantees that all sparse label combinations with up to $k$ active labels are argmaxable.
1 code implementation • ACL 2022 • Andreas Grivas, Nikolay Bogoychev, Adam Lopez
Classifiers in natural language processing (NLP) often have a large number of output classes.
no code implementations • 18 Feb 2021 • Arlene Casey, Emma Davidson, Michael Poon, Hang Dong, Daniel Duma, Andreas Grivas, Claire Grover, Víctor Suárez-Paniagua, Richard Tobin, William Whiteley, Honghan Wu, Beatrice Alex
Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited.
no code implementations • EMNLP 2018 • Clara Vania, Andreas Grivas, Adam Lopez
When parsing morphologically-rich languages with neural models, it is beneficial to model input at the character level, and it has been claimed that this is because character-level models learn morphology.