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    <titleInfo>
        <title>Decipherment of Substitution Ciphers with Neural Language Models</title>
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    <name type="personal">
        <namePart type="given">Nishant</namePart>
        <namePart type="family">Kambhatla</namePart>
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    <name type="personal">
        <namePart type="given">Anahita</namePart>
        <namePart type="family">Mansouri Bigvand</namePart>
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    <name type="personal">
        <namePart type="given">Anoop</namePart>
        <namePart type="family">Sarkar</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>Decipherment of homophonic substitution ciphers using language models is a well-studied task in NLP. Previous work in this topic scores short local spans of possible plaintext decipherments using n-gram language models. The most widely used technique is the use of beam search with n-gram language models proposed by Nuhn et al.(2013). We propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. We augment beam search with a novel rest cost estimation that exploits the prediction power of a neural language model. We compare against the state of the art n-gram based methods on many different decipherment tasks. On challenging ciphers such as the Beale cipher we provide significantly better error rates with much smaller beam sizes.</abstract>
    <identifier type="citekey">kambhatla-etal-2018-decipherment</identifier>
    <identifier type="doi">10.18653/v1/D18-1102</identifier>
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        <date>2018-oct-nov</date>
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            <start>869</start>
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%0 Conference Proceedings
%T Decipherment of Substitution Ciphers with Neural Language Models
%A Kambhatla, Nishant
%A Mansouri Bigvand, Anahita
%A Sarkar, Anoop
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kambhatla-etal-2018-decipherment
%X Decipherment of homophonic substitution ciphers using language models is a well-studied task in NLP. Previous work in this topic scores short local spans of possible plaintext decipherments using n-gram language models. The most widely used technique is the use of beam search with n-gram language models proposed by Nuhn et al.(2013). We propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. We augment beam search with a novel rest cost estimation that exploits the prediction power of a neural language model. We compare against the state of the art n-gram based methods on many different decipherment tasks. On challenging ciphers such as the Beale cipher we provide significantly better error rates with much smaller beam sizes.
%R 10.18653/v1/D18-1102
%U https://aclanthology.org/D18-1102
%U https://doi.org/10.18653/v1/D18-1102
%P 869-874

Markdown (Informal)

[Decipherment of Substitution Ciphers with Neural Language Models](https://aclanthology.org/D18-1102) (Kambhatla et al., EMNLP 2018)

• Decipherment of Substitution Ciphers with Neural Language Models (Kambhatla et al., EMNLP 2018)

ACL

• Nishant Kambhatla, Anahita Mansouri Bigvand, and Anoop Sarkar. 2018. Decipherment of Substitution Ciphers with Neural Language Models. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 869–874, Brussels, Belgium. Association for Computational Linguistics.