@inproceedings{nishino-etal-2019-generating,
    title = "Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints",
    author = "Nishino, Masaaki  and
      Takase, Sho  and
      Hirao, Tsutomu  and
      Nagata, Masaaki",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D19-1674/",
    doi = "10.18653/v1/D19-1674",
    pages = "6408--6412",
    abstract = "An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, {\textquotedblleft}Trims cash{\textquotedblright} is an anagram of {\textquotedblleft}Christmas{\textquotedblright}. Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods."
}