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.

本文提出了伯克利填字游戏求解器，该系统使用神经问答模型生成填字谜面的答案候选，并将循环置信传播与本地搜索相结合，以找到完整的谜底解。与现有方法相比，我们的系统使得填字准确率从 71% 提高到 82%。此外，我们还分析了系统的剩余错误并发布了一个包含超过 600 万个问题答案对的数据集。