Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles

In this paper, we study the impact of rela- tional and syntactic representations for an interesting and challenging task: the au- tomatic resolution of crossword puzzles. Automatic solvers are typically based on two answer retrieval modules: (i) a web search engine, e.g., Google, Bing, etc. and (ii) a database (DB) system for access- ing previously resolved crossword puz- zles. We show that learning to rank models based on relational syntactic structures de- fined between the clues and the answer can improve both modules above. In particu- lar, our approach accesses the DB using a search engine and reranks its output by modeling paraphrasing. This improves on the MRR of previous system up to 53% in ranking answer candidates and greatly im- pacts on the resolution accuracy of cross- word puzzles up to 15%.