natural-language-processing-11

Decipherment

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Datasets

Chinese Gigaword

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Most implemented papers

A Probabilistic Formulation of Unsupervised Text Style Transfer

Graham Neubig, Junxian He, Taylor Berg-Kirkpatrick•Sun Feb 09 2020

A deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques and demonstrates the effectiveness of the method on a wide range of unsuper supervised style transfer tasks, including sentiment transfer, formality transfer, word decipherment, author imitation, and related language translation.

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Style Transfer from Non-Parallel Text by Cross-Alignment

T. Jaakkola, R. Barzilay, Tao Lei, T. Shen•Sun Apr 30 2017

This paper proposes a method that leverages refined alignment of latent representations to perform style transfer on the basis of non-parallel text, and demonstrates the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.

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A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages

Heng Ji, Jonathan May, Ronald Cardenas, Ying Lin•Tue Apr 09 2019

This work describes an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data and demonstrates the tagger’s utility by incorporating it into a true ‘zero-resource’ variant of the MALOPA dependency parser model.

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Decipherment of Historical Manuscript Images

Xusen Yin, Nada Aldarrab, Beáta Megyesi, Kevin Knight•Mon Oct 08 2018

This work develops unsupervised models for character segmentation, character-image clustering, and decipherment of cluster sequences for enciphered manuscripts and gives empirical results for multiple ciphers.

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Unsupervised Text Style Transfer using Language Models as Discriminators

E. Xing, Chris Dyer, Zhiting Hu, Taylor Berg-Kirkpatrick, Zichao Yang•Tue May 29 2018

This paper proposes a new technique that uses a target domain language model as the discriminator, providing richer and more stable token-level feedback during the learning process, and shows that this approach leads to improved performance on three tasks: word substitution decipherment, sentiment modification, and related language translation.

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Sign Clustering and Topic Extraction in Proto-Elamite

Logan Born, Kate Kelley, Nishant Kambhatla, Carolyn Chen, Anoop Sarkar•Fri May 31 2019

This first attempt at using techniques from computational linguistics to analyze the undeciphered proto-Elamite script using hierarchical clustering, n-gram frequencies, and LDA topic models demonstrates the utility of these techniques as an aid to manual decipherment.

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Phonetic and Visual Priors for Decipherment of Informal Romanization

Maria Ryskina, Matthew R. Gormley, Taylor Berg-Kirkpatrick•Thu Apr 30 2020

A noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion and demonstrates that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model’s performance on both languages, yielding results much closer to the supervised skyline.

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Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B

R. Barzilay, Yuan Cao, Jiaming Luo•Sat Jun 15 2019

A novel neural approach for automatic decipherment of lost languages with first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where the model correctly translates 67.3% of cognates.

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Deciphering Undersegmented Ancient Scripts Using Phonetic Prior

R. Barzilay, Yuan Cao, Enrico Santus, Jiaming Luo, F. Hartmann•Tue Oct 20 2020

A decipherment model that handles both of these challenges by building on rich linguistic constraints reflecting consistent patterns in historical sound change is proposed, and a measure for language closeness which correctly identifies related languages for Gothic and Ugaritic is proposed.

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