  Cryptic crosswords, the dominant English-language crossword variety in the
United Kingdom, can be solved by expert humans using flexible, creative
intelligence and knowledge of language. Cryptic clues read like fluent natural
language, but they are adversarially composed of two parts: a definition and a
wordplay cipher requiring sub-word or character-level manipulations. As such,
they are a promising target for evaluating and advancing NLP systems that seek
to process language in more creative, human-like ways. We present a dataset of
cryptic crossword clues from a major newspaper that can be used as a benchmark
and train a sequence-to-sequence model to solve them. We also develop related
benchmarks that can guide development of approaches to this challenging task.
We show that performance can be substantially improved using a novel curriculum
learning approach in which the model is pre-trained on related tasks involving,
e.g, unscrambling words, before it is trained to solve cryptics. However, even
this curricular approach does not generalize to novel clue types in the way
that humans can, and so cryptic crosswords remain a challenge for NLP systems
and a potential source of future innovation.
