论文标题
自动填字游戏解决
Automated Crossword Solving
论文作者
论文摘要
我们提出伯克利填字游戏求解器，这是一种自动解决填字游戏的最新方法。我们的系统通过使用神经问题答案模型为每个填字游戏生成答案候选者，然后将循环信念传播与本地搜索结合起来，以找到完整的拼图解决方案。与现有方法相比，我们的系统将精确的拼图准确性从《纽约时报》的填字游戏中提高到82％，并获得了无主题难题的99.9％的字母准确性。此外，在2021年，我们系统的混合动力车和现有的Dr.Fill System在美国填字游戏中首次优于所有人类竞争对手。为了促进问题回答和填字游戏解决，我们分析了系统的剩余错误，并发布了超过600万个问答对的数据集。
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.
