Lazy-k: Decoding for Constrained Token Classification

Autor:	Hemmer, Arthur, Coustaty, Mickaël, Bartolo, Nicola, Brachat, Jérôme, Ogier, Jean-Marc
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted EMNLP Main 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.03367 Zobrazit plný text záznamu View this record from Arxiv