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Cryptic crosswords, the dominant crossword variety in the UK, are a promisingtarget for advancing NLP systems that seek to process semantically complex,highly compositional language. Cryptic clues read like fluent natural languagebut are adversarially composed of two parts: a definition and a wordplay cipherrequiring character-level manipulations. Expert humans use creativeintelligence to solve cryptics, flexibly combining linguistic, world, anddomain knowledge. In this paper, we make two main contributions. First, wepresent a dataset of cryptic clues as a challenging new benchmark for NLPsystems that seek to process compositional language in more creative,human-like ways. After showing that three non-neural approaches and T5, astate-of-the-art neural language model, do not achieve good performance, wemake our second main contribution: a novel curriculum approach, in which themodel is first fine-tuned on related tasks such as unscrambling words.We alsointroduce a challenging data split, examine the meta-linguistic capabilities ofsubword-tokenized models, and investigate model systematicity by perturbing thewordplay part of clues, showing that T5 exhibits behavior partially consistentwith human solving strategies. Although our curricular approach considerablyimproves on the T5 baseline, our best-performing model still fails togeneralize to the extent that humans can. Thus, cryptic crosswords remain anunsolved challenge for NLP systems and a potential source of future innovation.

Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP