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        <title>Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints</title>
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    <name type="personal">
        <namePart type="given">Masaaki</namePart>
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        <namePart type="given">Sho</namePart>
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    <name type="personal">
        <namePart type="given">Masaaki</namePart>
        <namePart type="family">Nagata</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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    <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, “Trims cash” is an anagram of “Christmas”. 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.</abstract>
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%0 Conference Proceedings
%T Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints
%A Nishino, Masaaki
%A Takase, Sho
%A Hirao, Tsutomu
%A Nagata, Masaaki
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F nishino-etal-2019-generating
%X An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, “Trims cash” is an anagram of “Christmas”. 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.
%R 10.18653/v1/D19-1674
%U https://aclanthology.org/D19-1674
%U https://doi.org/10.18653/v1/D19-1674
%P 6408-6412

Markdown (Informal)

[Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints](https://aclanthology.org/D19-1674) (Nishino et al., EMNLP-IJCNLP 2019)

• Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints (Nishino et al., EMNLP-IJCNLP 2019)

ACL

• Masaaki Nishino, Sho Takase, Tsutomu Hirao, and Masaaki Nagata. 2019. Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6408–6412, Hong Kong, China. Association for Computational Linguistics.