Die Rätselrevolution: Automated German Crossword Solving
Authors: Andrea Zugarini, Thomas Röthenbacher, Kai Klede, Marco Ernandes, Bjoern Eskofier, Dario Zanca (expert.ai, Friedrich-Alexander-Universität Erlangen-Nürnberg, German Research Center for Environmental Health, 2023)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Crossword puzzles are popular word games played in various languages around the world, with diverse styles across different countries. For this reason, automated crossword solvers designed for a language, may not work well on others. In this paper, we extend Webcrow, an automatic crossword solver, to German, making it the first program for crossword solving in the German language. To address the lack of large clue-answer crossword pairs data, Webcrow combines multiple modules, known as experts, which retrieve potential answers from various resources, including the web, knowledge graphs, and linguistic rules. The system is evaluated on a collection of crosswords from variegate sources, where it is able to solve perfectly 67% of them. Additional analysis reveals that while our solver achieved commendable results, puzzles with poorly constrained schemas and original clues still presented significant hurdles. These findings shed light on the complexity of the crossword-solving problem and emphasize the need for future research to address and overcome these particular challenges effectively.

Introduction
Automated crossword solving is a challenging problem in Artificial Intelligence and Natural Language Processing (NLP) research. Solving a puzzle requires multiple skills, ranging from encyclopedic knowledge and linguistics to reasoning and constraint satisfaction. In the past, several automated crossword systems have been proposed for English [1, 2, 3]. Despite the successful results achieved by these approaches, they do not investigate crossword resolution in other languages. All those methods heavily rely on large databases of previously answered clues to retrieve and rank answer candidates, that sometimes are even re-ranked [4, 5, 6, 7]. Berkley Crossword Solver [3] make also use of multiple Language Models that have been fine-tuned to segment answers in words and to correct wrong letters. The need for such resources hinders the application of these solutions to other languages. WebCrow [8, 9] instead, is a crossword solver that was applied also to Italian puzzles. The architecture which is composed of multiple modules, called experts, facilitates the portability to new languages. In this work, we extend WebCrow to the German language. To the best of our knowledge, we are the first to propose an automatic solver for German crosswords. The paper is organized as follows. In Section 2, the whole WebCrow architecture is described. Then, in Section 3 we present the data gathered for German and its usage by the WebCrow experts. Experiments are outlined in Section 4. Finally, we summarize our conclusions and directions for future works in Section 5.

Conclusion
In this work we presented German Webcrow, the first crossword puzzle solver for the German Language. We collected both a dataset of clue-answer pairs and a set of German crosswords from different sources having various formats and styles. Webcrow achieved near-perfect word accuracy in Swedish-type crosswords, that proved to be generally easy to solve, solving overall 39/58 perfectly. However, our solver performed poorly on FAZ crosswords. Those puzzles were extremely challenging for multiple reasons, such as the poorly constrained schemas, due to the presence of many black cells, and the rich presence of sophisticated, original clues, involving articulated wordplays that formed words not present in the candidate answers lists. Challenging puzzles like the ones in FAZ are a clear example of how complex the problem is, and why studying crosswords in multiple languages and formats is important for automated crossword solving. In the future we plan to address the current limitations. In particular, we plan to investigate the use of generative models, to cope with the novel unseen clues.