Lazy-k: Decoding for Constrained Token Classification
Arthur Hemmer Mickaël Coustaty Nicola Bartolo Jérôme Brachat Jean-Marc Ogier
We explore the possibility of improving probabilistic models in structured
prediction. Specifically, we combine the models with constrained decoding
approaches in the context of token classification for information extraction.
The decoding methods search for constraint-satisfying label-assignments while
maximizing the total probability. To do this, we evaluate several existing
approaches, as well as propose a novel decoding method called Lazy-$k$. Our
findings demonstrate that constrained decoding approaches can significantly
improve the models' performances, especially when using smaller models. The
Lazy-$k$ approach allows for more flexibility between decoding time and
accuracy. The code for using Lazy-$k$ decoding can be found here:
https://github.com/ArthurDevNL/lazyk.