| Challenge: | Existing methods for deciphering historical substitution ciphers are difficult to crack . cipheries that are not segmented are still difficult to deciphere . |
| Approach: | They propose automatic methods to segment historical substitution ciphers using BPE and unigram language models. |
| Outcome: | The proposed methods achieve an average segmentation error of 2% on 100 monoalphabetic ciphers and 27% on 3 real historical homophonic cipheries. |
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Can Sequence-to-Sequence Models Crack Substitution Ciphers? (2021.acl-long)
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| Challenge: | Current methods for deciphering historical ciphers use beam search and a neural language model . but, this approach assumes that the target plaintext language is known . |
| Approach: | They propose an end-to-end multilingual decipherment model that can solve 1:1 substitution ciphers without explicit language identification. |
| Outcome: | The proposed model can decipher text without explicit language identification while still being robust to noise. |
Decipherment of Substitution Ciphers with Neural Language Models (D18-1)
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| Challenge: | Existing methods for deciphering homophonic substitution ciphers use pre-trained neural LMs. |
| Approach: | They propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. |
| Outcome: | The proposed beam search algorithm improves on challenging ciphers with smaller beam sizes and better error rates than state-of-the-art methods. |
Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations (2023.findings-eacl)
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| Challenge: | Existing methods for solving substitution ciphers use character-level language models to find key . a Transformer-based causal language model can be used to learn recurrences between characters in a ciphered text . |
| Approach: | They propose a Transformer-based causal language model for decipherment that learns recurrences between characters in a ciphertext. |
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Solving Historical Dictionary Codes with a Neural Language Model (2020.emnlp-main)
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| Challenge: | a dictionary-based substitution code is common, but no automatic decipherment algorithms exist. |
| Approach: | They propose a decoding lattice and a neural language model to solve word-based substitution codes . they apply their method to letters exchanged between general James Wilkinson and agents of the Spanish Crown . |
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CWSeg: An Efficient and General Approach to Chinese Word Segmentation (2023.acl-industry)
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| Challenge: | Existing methods for Chinese word segmentation have achieved state-of-the-art performance, but they pose challenges in the deployment. |
| Approach: | They propose to augment PLM-based Chinese word segmentation schemes by developing cohort training and versatile decoding strategies. |
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Towards Universal Segmentations: UniSegments 1.0 (2022.lrec-1)
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Zdeněk Žabokrtský, Niyati Bafna, Jan Bodnár, Lukáš Kyjánek, Emil Svoboda, Magda Ševčíková, Jonáš Vidra
| Challenge: | Existing data resources for morphological segmentation are limited to 32 languages . a large number of word forms exist, with some sub-parts being "recycled" many times . |
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BPE-Dropout: Simple and Effective Subword Regularization (2020.acl-main)
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| Challenge: | Subword segmentation is widely used to address the open vocabulary problem in machine translation. |
| Approach: | They propose a method that stochastically corrupts the segmentation procedure of BPE and produces multiple segmentations within the same fixed BPE framework. |
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BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages (2022.findings-acl)
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| Challenge: | Morphologically rich polysynthetic languages present a challenge for NLP systems due to data sparsity. |
| Approach: | They propose to use subword segmentation to reduce data sparsity in polysynthetic languages . they compare supervised and unsupervised morphological segmentation methods to Byte-Pair Encodings . |
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Byte Pair Encoding is Suboptimal for Language Model Pretraining (2020.findings-emnlp)
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| Challenge: | Subword tokenization is a popular language model that can be used to segment text. |
| Approach: | They analyze differences between byte-pair encoding (BPE) and unigram LM tokenization methods to find subword units that align more closely with morphology. |
| Outcome: | The proposed method recovers subword units that align more closely with morphology and avoids problems stemming from BPE’s greedy construction procedure. |
LLMSegm: Surface-level Morphological Segmentation Using Large Language Model (2024.lrec-main)
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| Challenge: | Existing approaches to morphological segmentation split word into its morphemes . LLMSegm is applicable in low-data settings and low-resourced languages . |
| Approach: | They propose a novel approach to surface-level morphological segmentation leveraging large language models. |
| Outcome: | The proposed method is applicable in low-data settings and low-resource languages. |