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.
翻译:我们展示了伯克利交错解答器,这是自动解答填字谜的最先进的方法。我们的系统通过使用神经问题解答模型为每个填字线索生成解答对象,然后将循环的信仰传播与本地搜索相结合,以寻找完整的解谜解决方案。与现有的方法相比,我们的系统将《纽约时报》填字游戏的精确拼图精确度从71%提高到82%,并在无主题解谜上获得了99.9%的字母准确度。此外,2021年,我们系统的混合体和现有的Fill博士系统首次在美国交错词拼字游戏上超越了所有人类竞争者。为了便利对问题解答和拼字解答的研究,我们分析了我们的系统剩余错误,并发布了600多万对问答的数据集。