English

Automated Crossword Solving

Computation and Language 2022-07-05 v2

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 71% to 82% on crosswords from The New York Times and obtains 99.9% letter accuracy on themeless puzzles. Additionally, in 2021, a hybrid of our system and the existing Dr.Fill system outperformed all human competitors for the first time at the American Crossword Puzzle Tournament. 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.

Keywords

Cite

@article{arxiv.2205.09665,
  title  = {Automated Crossword Solving},
  author = {Eric Wallace and Nicholas Tomlin and Albert Xu and Kevin Yang and Eshaan Pathak and Matthew Ginsberg and Dan Klein},
  journal= {arXiv preprint arXiv:2205.09665},
  year   = {2022}
}

Comments

ACL 2022

R2 v1 2026-06-24T11:22:31.678Z