Algorithms for Weighted Pushdown Automata
Alexandra Butoi, Brian DuSell, Tim Vieira, Ryan Cotterell, David Chiang
Abstract
Weighted pushdown automata (WPDAs) are at the core of many natural language processing tasks, like syntax-based statistical machine translation and transition-based dependency parsing. As most existing dynamic programming algorithms are designed for context-free grammars (CFGs), algorithms for PDAs often resort to a PDA-to-CFG conversion. In this paper, we develop novel algorithms that operate directly on WPDAs. Our algorithms are inspired by Lang’s algorithm, but use a more general definition of pushdown automaton and either reduce the space requirements by a factor of |Gamma| (the size of the stack alphabet) or reduce the runtime by a factor of more than |Q| (the number of states). When run on the same class of PDAs as Lang’s algorithm, our algorithm is both more space-efficient by a factor of |Gamma| and more time-efficient by a factor of |Q| x |Gamma|.
BibTeX
@inproceedings{butoi-etal-2022-algorithms,
title = "Algorithms for Weighted Pushdown Automata",
author = "Butoi, Alexandra and
DuSell, Brian and
Vieira, Tim and
Cotterell, Ryan and
Chiang, David",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.656/",
doi = "10.18653/v1/2022.emnlp-main.656",
pages = "9669--9680"
}