Common term for benchmark crossword clue1/22/2024 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 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. Publisher = "Association for Computational Linguistics",Ībstract = "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. Cite (Informal): Down and Across: Introducing Crossword-Solving as a New NLP Benchmark (Kulshreshtha et al., ACL 2022) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Code = "Down and Across: Introducing Crossword-Solving as a New Benchmark",īooktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", Association for Computational Linguistics. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2648–2659, Dublin, Ireland. Down and Across: Introducing Crossword-Solving as a New NLP Benchmark. Anthology ID: 2022.acl-long.189 Volume: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Month: May Year: 2022 Address: Dublin, Ireland Editors: Smaranda Muresan,Īline Villavicencio Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 2648–2659 Language: URL: DOI: 10.18653/v1/2022.acl-long.189 Bibkey: kulshreshtha-etal-2022-across Cite (ACL): Saurabh Kulshreshtha, Olga Kovaleva, Namrata Shivagunde, and Anna Rumshisky. 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. Abstract 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.
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