  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. In this work,
we introduce solving crossword puzzles as a new natural language understanding
task. 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. 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 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. For the
question answering task, our baselines include several sequence-to-sequence and
retrieval-based generative models. We also introduce a non-parametric
constraint satisfaction baseline for solving the entire crossword puzzle.
Finally, we propose an evaluation framework which consists of several
complementary performance metrics.

Computation and Language;Artificial Intelligence