Lazy-k Decoding: Constrained Decoding for Information Extraction

Arthur Hemmer, Mickael Coustaty, Nicola Bartolo, Jerome Brachat, Jean-marc Ogier

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Submission Track 2: NLP Applications

Keywords: structured prediction, constrained decoding, token classification, information extraction, beam search

TL;DR: We present a flexible constrained information extraction method combined with probabilistic models

Abstract: 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 at https://github.com/ArthurDevNL/lazyk.

Submission Number: 1790