Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. We propose PICARD (code and trained models available at https://github.com/ElementAI/picard), a method for constraining auto-regressive decoders of language models through incremental parsing. PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step. On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into state-of-the-art solutions.
@article{arxiv.2109.05093,
title = {PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models},
author = {Torsten Scholak and Nathan Schucher and Dzmitry Bahdanau},
journal= {arXiv preprint arXiv:2109.05093},
year = {2021}
}