解密密码纵横字谜：将语义复杂的文字游戏谜题作为NLP的目标
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP
AI总结：
本文提出密码线索数据集作为NLP新基准，通过课程学习方法（先在单词重组等任务微调）改进T5，虽未达人类泛化能力，但为处理组合性语言提供新方向。
摘要：
密码纵横字谜是英国最主要的字谜类型，对于推进旨在处理语义复杂、高度组合性语言的NLP系统而言，是一个很有前景的目标。密码线索读起来像流畅的自然语言，但由两部分对抗性地构成：定义部分和需要字符级操作的文字游戏密码。专业人类利用创造性智能解决密码纵横字谜，灵活结合语言、世界和领域知识。在本文中，我们有两个主要贡献。首先，我们提出了一个密码线索数据集，作为NLP系统的挑战性新基准，这些系统旨在以更具创造性、类人的方式处理组合性语言。在展示了三种非神经方法和最先进的神经语言模型T5未取得良好性能后，我们做出了第二个主要贡献：一种新颖的课程学习方法，其中模型首先在相关任务（如单词重组）上进行微调。我们还引入了一个具有挑战性的数据分割，检查了子词标记化模型的元语言能力，并通过扰动线索的文字游戏部分来研究模型的系统性，结果表明T5表现出部分与人类解题策略一致的行为。尽管我们的课程学习方法显著改进了T5基线，但我们性能最佳的模型仍然无法像人类那样广泛泛化。因此，密码纵横字谜仍然是NLP系统尚未解决的挑战，也是未来创新的潜在来源。
英文摘要：
Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language. Cryptic clues read like fluent natural language but are adversarially composed of two parts: a definition and a wordplay cipher requiring character-level manipulations. Expert humans use creative intelligence to solve cryptics, flexibly combining linguistic, world, and domain knowledge. In this paper, we make two main contributions. First, we present a dataset of cryptic clues as a challenging new benchmark for NLP systems that seek to process compositional language in more creative, human-like ways. After showing that three non-neural approaches and T5, a state-of-the-art neural language model, do not achieve good performance, we make our second main contribution: a novel curriculum approach, in which the model is first fine-tuned on related tasks such as unscrambling words.We also introduce a challenging data split, examine the meta-linguistic capabilities of subword-tokenized models, and investigate model systematicity by perturbing the wordplay part of clues, showing that T5 exhibits behavior partially consistent with human solving strategies. Although our curricular approach considerably improves on the T5 baseline, our best-performing model still fails to generalize to the extent that humans can. Thus, cryptic crosswords remain an unsolved challenge for NLP systems and a potential source of future innovation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
