Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Finally, we propose an evaluation framework which consists of several complementary performance metrics. We also introduce a non-parametric constraint satisfaction baseline for solving the entire crossword puzzle. For the question answering task, our baselines include several sequence-to-sequence and retrieval-based generative models. We separately release the clue-answer pairs from these puzzles as an open-domain question answering dataset containing over half a million unique clue-answer pairs. These puzzles include a diverse set of clues: historic, factual, word meaning, synonyms/antonyms, fill-in-the-blank, abbreviations, prefixes/suffixes, wordplay, and cross-lingual, as well as clues that depend on the answers to other clues. We release the specification of a corpus of crossword puzzles collected from the New York Times daily crossword spanning 25 years and comprised of a total of around nine thousand puzzles. In this work, we introduce solving crossword puzzles as a new natural language understanding task. Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount of knowledge about language and the world, and the ability to satisfy the constraints imposed by the structure of the puzzle. Finally, a few example puzzles generated by our algorithm are also provided. The techniques have been found to be successful in quickly producing well-packed puzzles of even large sizes. An end-to-end algorithm that combines these strategies is presented, and its performance is analyzed. The strategies have been formulated based on a study of the effect of word sequence permutation order on grid fitting. The strategies proposed cover the tasks of selection of words from a given vocabulary, selection of grid sizes, grid resizing and adjustments, metrics for word fitting, back-tracking techniques, and also clue generation. This paper discusses algorithmic strategies for automatic crossword puzzle generation in such an unconstrained setting. Hence, it not only requires the algorithm to determine the word locations, but it also needs to come up with the grid geometry. In this problem, only the word vocabulary, and optionally the grid dimensions are known. An unconstrained crossword puzzle is a generalization of the constrained crossword problem.
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