Published in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
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 constraintsatisfying 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.
Recommended citation: Hemmer, Arthur, Mickaël Coustaty, Nicola Bartolo, Jérôme Brachat, and Jean-Marc Ogier. "Lazy-k Decoding: Constrained Decoding for Information Extraction." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 6727-6736. 2023. https://aclanthology.org/2023.emnlp-main.416