Automated Crossword Solving
Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew Ginsberg, Dan Klein
Abstract
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles. Our system works by generating answer candidates for each crossword clue using neural question answering models and then combines loopy belief propagation with local search to find full puzzle solutions. Compared to existing approaches, our system improves exact puzzle accuracy from 57% to 82% on crosswords from The New York Times and obtains 99.9% letter accuracy on themeless puzzles. Our system also won first place at the top human crossword tournament, which marks the first time that a computer program has surpassed human performance at this event. To facilitate research on question answering and crossword solving, we analyze our system’s remaining errors and release a dataset of over six million question-answer pairs.
BibTeX
@inproceedings{wallace-etal-2022-automated,
title = "Automated Crossword Solving",
author = "Wallace, Eric and
Tomlin, Nicholas and
Xu, Albert and
Yang, Kevin and
Pathak, Eshaan and
Ginsberg, Matthew and
Klein, Dan",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.219/",
doi = "10.18653/v1/2022.acl-long.219",
pages = "3073--3085"
}