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。

自动填字游戏求解

Automated Crossword Solving

The conventional paradigm in neural question answering (QA) for narrative
content is limited to a two-stage process: first, relevant text passages are
retrieved and, subsequently, a neural network for machine comprehension
extracts the likeliest answer. However, both stages are largely isolated in the
status quo and, hence, information from the two phases is never properly fused.
In contrast, this work proposes RankQA: RankQA extends the conventional
two-stage process in neural QA with a third stage that performs an additional
answer re-ranking. The re-ranking leverages different features that are
directly extracted from the QA pipeline, i.e., a combination of retrieval and
comprehension features. While our intentionally simple design allows for an
efficient, data-sparse estimation, it nevertheless outperforms more complex QA
systems by a significant margin: in fact, RankQA achieves state-of-the-art
performance on 3 out of 4 benchmark datasets. Furthermore, its performance is
especially superior in settings where the size of the corpus is dynamic. Here
the answer re-ranking provides an effective remedy against the underlying
noise-information trade-off due to a variable corpus size. As a consequence,
RankQA represents a novel, powerful, and thus challenging baseline for future
research in content-based QA.

RankQA 是一种新的神经问答模型，利用三阶段过程（检索、理解和重新排序）来自动回答问题，为后续基于内容的问答研究提供了有力的基线模型。