---
title: "Deciphering Undersegmented Ancient Scripts Using Phonetic Prior — Luo 2021"
description: "The paper asks how to match an unsegmented or undersegmented script against the vocabulary of a known language when… 16 references, cited by 9 papers here."
authors: ["Jiaming Luo", "Frederik Hartmann", "Enrico Santus", "Regina Barzilay", "Yuan Cao"]
year: 2021
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2021.tacl-1.5/"
doi: "10.1162/tacl_a_00354"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/luo-2021-deciphering-undersegmented-ancient-scripts-phonetic/"
---
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# Deciphering Undersegmented Ancient Scripts Using Phonetic Prior

**Authors:** [Jiaming Luo](https://md.research.elamur.ai/free/en/authors/jiaming-luo/); Frederik Hartmann; [Enrico Santus](https://md.research.elamur.ai/free/en/authors/enrico-santus/); [Regina Barzilay](https://md.research.elamur.ai/free/en/authors/regina-barzilay/); [Yuan Cao](https://md.research.elamur.ai/free/en/authors/yuan-cao/) · **Year:** 2021 · **Venue:** Transactions of the Association for Computational Linguistics, vol. 9, pp. 69–81 **Link:** [aclanthology.org/2021.tacl-1.5](https://aclanthology.org/2021.tacl-1.5/) · library `tacl-2021-luo-deciphering-undersegmented-ancient-scripts-phonetic` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**20** citations](https://openalex.org/works?filter=cites:W3129293584) OpenAlex, 2026-10-14 · [**9** citations in our library](https://md.research.elamur.ai/free/en/papers/luo-2021-deciphering-undersegmented-ancient-scripts-phonetic/cited-by/) of the 140,884 papers whose bibliographies we read · [Transactions of the Association for Computational Linguistics](https://aclanthology.org/volumes/2021.tacl-1/) · [licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2021.tacl-1.5/)
- [PDF](https://aclanthology.org/2021.tacl-1.5.pdf)
- [DOI 10.1162/tacl_a_00354](https://doi.org/10.1162/tacl_a_00354)
- [OpenAlex](https://openalex.org/W3129293584)

**Authors (5)**

1. [Jiaming Luo](https://openalex.org/A5029740891) [Semantic Scholar](https://www.semanticscholar.org/author/19236313) · h 4 · 74 citations · 8 works
2. [Frederik Hartmann](https://openalex.org/A5003018532) [ORCID](https://orcid.org/0000-0002-6649-5371) · [Semantic Scholar](https://www.semanticscholar.org/author/46486499) · h 6 · 98 citations · 30 works
3. [Enrico Santus](https://openalex.org/A5059288410) [ORCID](https://orcid.org/0000-0002-7327-2731) · [Semantic Scholar](https://www.semanticscholar.org/author/150991377) · h 21 · 1,507 citations · 100 works
4. [Regina Barzilay](https://openalex.org/A5010124873) [ORCID](https://orcid.org/0000-0002-2921-8201) · [Semantic Scholar](https://www.semanticscholar.org/author/1741283) · h 91 · 34,823 citations · 358 works
5. [Yuan Cao](https://openalex.org/A5101989491) [ORCID](https://orcid.org/0000-0002-2495-4701) · [Semantic Scholar](https://www.semanticscholar.org/author/145144022) · h 28 · 9,158 citations · 84 works

Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart.

## What it is about

The paper asks how to match an unsegmented or undersegmented script against the vocabulary of a known language when the language family of the lost script is not known. The model jointly segments the text and aligns each matched span to a known stem with a monotonic, edit-distance-style alignment. Characters are embedded from their IPA features, so that similar sounds get similar vectors, and a penalty keeps the lost script from losing sounds (each lost symbol should map to one known sound). It also proposes a closeness measure: coverage of the lost text against a known language's vocabulary, plotted against confidence. On deciphered test cases the full model scores A@10 of 0.661 averaged over settings for Gothic (0.419 with no phonological knowledge), and it finds Proto-Germanic as the closest known language, with 75 percent A@10 even with no prior knowledge. On Iberian personal names it reaches 75 percent top-10 with full knowledge; the closeness measure does not single out Basque.

## For Mova

- I1. Closeness by coverage: for a text in one Aramaic variety, match its words against each other variety's stem list and plot coverage against match confidence. This gives an empirical test of which variety (`languages/arc-x-targum`, `languages/tmr`, `languages/jpa`) is nearest, and it can be checked against the known history.
- I2. Monotonic alignment with insertion penalty: align consonant skeletons of cognate forms with substitution, deletion and insertion costs, where insertion is penalised by a parameter alpha. Use it to match a word in one variety with its counterpart in another, and as a distance for `tools/ocrfix` repairs.
- I3. Sound-inventory check: require each consonant of a variety to correspond to a consistent consonant of the known language, and flag the cases where one letter maps to several sounds or where a sound disappears. Write the resulting table into [languages/arc/script-phonology.md](https://mova.elamur.ai/free/en/docs/languages/arc/script-phonology/).

I1 belongs in [languages/arc/family.md](https://mova.elamur.ai/free/en/docs/languages/arc/family/) and in the closeness tests for the Aramaic varieties. I2 is an alignment routine for `tools/ocrfix` and for cognate lookup across `languages/arc`. I3 is a check for [languages/arc/script-phonology.md](https://mova.elamur.ai/free/en/docs/languages/arc/script-phonology/). The decipherment goal itself is outside Mova; the parts that transfer are the alignment, the closeness measure and the inventory check.

## Institutions

[🏢**Google** Mountain View · United States 272 works · 255,532 citations](https://md.research.elamur.ai/free/en/companies/google/) · [🎓**Massachusetts Institute of Technology** Cambridge · United States 125 works · 60,031 citations](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) · [🔬**MIT Computer Science and Artificial Intelligence Laboratory** Cambridge · United States 45 works · 26,807 citations](https://md.research.elamur.ai/free/en/labs/mit-computer-science-and-artificial-intelligence-laboratory/) · [🏢**Bayer** Leverkusen · Germany 2 works · 387 citations](https://md.research.elamur.ai/free/en/companies/bayer/) · [🎓**University of Konstanz** Konstanz · Germany 6 works · 118 citations](https://md.research.elamur.ai/free/en/universities/university-of-konstanz/)

Also on the byline: [Bayer (United States)](https://ror.org/034ffbg36).

## Cite

Cite as Jiaming Luo, Frederik Hartmann, Enrico Santus, Regina Barzilay and Yuan Cao. 2021. Deciphering Undersegmented Ancient Scripts Using Phonetic Prior. Transactions of the Association for Computational Linguistics.

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/tacl_a_00354](https://doi.org/10.1162/tacl_a_00354)

```bibtex
@article{luo2021deciphering,
  title = {{Deciphering Undersegmented Ancient Scripts Using Phonetic Prior}},
  author = {Jiaming Luo, Frederik Hartmann, Enrico Santus, Regina Barzilay and Yuan Cao},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2021},
  doi = {10.1162/tacl\_a\_00354},
  url = {https://doi.org/10.1162/tacl_a_00354}
}
```

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- [next → Constrained Multi-Task Learning for Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/lu-2021-constrained-multi-task-learning-event/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/)

## References

16 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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2. Taylor Berg-Kirkpatrick and Dan Klein. 2013. Decipherment with a million random restarts. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 874–878. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Taylor+Berg%2DKirkpatrick+and+Dan+Klein%2E+2013%2E+Decipherment+with+a+million+random+restarts%2E+In+Proceedings+of+the+2013+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+874%E2%80%93878%2E+Asso)
3. Kevin Knight and Kenji Yamada. 1999. A computational approach to deciphering unknown scripts. Unsupervised Learning in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Kevin+Knight+and+Kenji+Yamada%2E+1999%2E+A+computational+approach+to+deciphering+unknown+scripts%2E+Unsupervised+Learning+in+Natural+Language+Processing%2E)
4. Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018a. Unsupervised machine translation using monolingual corpora only. [our card](https://md.research.elamur.ai/free/en/papers/lample-2017-unsupervised-machine-translation-using-monolingual/)
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8. Benjamin Snyder, Regina Barzilay, and Kevin Knight. 2010. A statistical model for lost language decipherment. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1048–1057, Uppsala, Sweden. Association for Computational Linguistics. DOI: https://doi.org/10.1093/oso/9780198792581.001.0001 [↗](https://doi.org/10.1093/oso/9780198792581.001.0001)
9. Jiaming Luo, Yuan Cao, and Regina Barzilay. 2019. Neural decipherment via minimum-cost flow: From ugaritic to linear b. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3146–3155, Florence, Italy. Association for Computational Linguistics. [our card](https://md.research.elamur.ai/free/en/papers/luo-2019-neural-decipherment-minimum-cost-flow/)
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11. Noemı́ Moncunill Martı́ and others. 2017. Indigenous naming practices in the western Mediterranean: The case of Iberian. Studia Antiqua et Archaeologica, 23(1):7–20. [find](https://scholar.google.com/scholar?q=Noem%C4%B1%CC%81+Moncunill+Mart%C4%B1%CC%81+and+others%2E+2017%2E+Indigenous+naming+practices+in+the+western+Mediterranean%3A+The+case+of+Iberian%2E+Studia+Antiqua+et+Archaeologica%2C+23%281%29%3A7%E2%80%9320%2E)
12. Larry Trask. 2008. Etymological Dictionary of Basque. [http://www.bulgari-istoria](http://www.bulgari-istoria) -2010.com/Rechnici/baski-rechnik .pdf. [↗](http://www.bulgari-istoria)
13. David R. Mortensen, Siddharth Dalmia, and Patrick Littell. 2018. Epitran: Precision G2P for many languages. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris, France. European Language Resources Association (ELRA). [our card](https://md.research.elamur.ai/free/en/papers/mortensen-2018-epitran-precision-g2p-many-languages/)
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