---
title: "Automated Phonological Transcription of Akkadian Cuneiform Text — Sahala 2020"
description: "Oracc holds many Akkadian texts only as sign-by-sign transliteration. 96% recall. 17 references, cited by 4 papers here."
authors: ["Aleksi Sahala", "Miikka Silfverberg", "Antti Arppe", "Krister Lindén"]
year: 2020
venue: "Proceedings of the Twelfth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2020.lrec-1.433/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sahala-2020-automated-phonological-transcription-akkadian-cuneiform/"
---
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# Automated Phonological Transcription of Akkadian Cuneiform Text

**Authors:** [Aleksi Sahala](https://md.research.elamur.ai/free/en/authors/aleksi-sahala/); [Miikka Silfverberg](https://md.research.elamur.ai/free/en/authors/miikka-silfverberg/); [Antti Arppe](https://md.research.elamur.ai/free/en/authors/antti-arppe/); [Krister Lindén](https://md.research.elamur.ai/free/en/authors/krister-linden/) · **Year:** 2020 · **Venue:** Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC 2020), pp. 3528–3534 **Link:** [aclanthology.org/2020.lrec-1.433](https://aclanthology.org/2020.lrec-1.433/) · library `lingbulk-2020-sahala-automated-phonological-transcription-akkadian-cuneiform` · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — summary only

[**6** citations](https://openalex.org/works?filter=cites:W3029527009) OpenAlex, 2026-10-14 · [**4** citations in our library](https://md.research.elamur.ai/free/en/papers/sahala-2020-automated-phonological-transcription-akkadian-cuneiform/cited-by/) of the 140,884 papers whose bibliographies we read · [Proceedings of the Twelfth Language Resources and Evaluation Conference](https://aclanthology.org/volumes/2020.lrec-1/) · [licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/2020.lrec-1.433/)
- [PDF](https://aclanthology.org/2020.lrec-1.433.pdf)
- [OpenAlex](https://openalex.org/W3029527009)

**Authors (4)**

1. [Aleksi Sahala](https://openalex.org/A5015516895) [ORCID](https://orcid.org/0000-0002-4255-3872) · [Semantic Scholar](https://www.semanticscholar.org/author/41032445) · h 6 · 90 citations · 48 works
2. [Miikka Silfverberg](https://openalex.org/A5027026724) [Semantic Scholar](https://www.semanticscholar.org/author/2614548) · h 17 · 1,038 citations · 100 works
3. [Antti Arppe](https://openalex.org/A5068156011) [ORCID](https://orcid.org/0000-0002-5977-4567) · [Semantic Scholar](https://www.semanticscholar.org/author/2587266) · h 15 · 1,247 citations · 99 works
4. [Krister Lindén](https://openalex.org/A5001408607) [ORCID](https://orcid.org/0000-0003-2337-303X) · [Semantic Scholar](https://www.semanticscholar.org/author/1787995) · h 21 · 1,752 citations · 225 works

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

## What it is about

Oracc holds many Akkadian texts only as sign-by-sign transliteration. The authors ask whether the phonological transcription (vowel lengths made explicit, logograms replaced by their Akkadian readings) can be produced automatically. The task splits in two: syllabic words are a grapheme-to-phoneme problem, and logograms are mostly a context-dependent lookup. They compare a majority-vote baseline, a statistical substitution model and three character-level encoder-decoders, one of which sees the neighbouring tokens. On a held-out split of about 34k tokens, syllabic tokens reach about 90% top-1 recall and 96% recall at 3, while logograms reach about 69% top-1 and 82% at 3. Fed to the rule-based analyser BabyFST, the output still yields the correct lemma and POS for about 94% of tokens when three candidates are kept.

## Ideas

- I2. Logograms as lookup with context cues: keep a lemma table for logograms and use the neighbouring words only to choose among its readings. The paper finds that context helps the top candidate by about 8 points and little beyond it. A rule that tries the top candidate with context and fills the rest from the frequency table would match that.
- I3. Audit the training pairs before learning from them: the paper notes that editors sometimes drop mimation, add morphemes or change case, and that a model then generalises these errors to unrelated words. A check that flags a pair whose consonant skeleton does not align with its transliteration is a cheap way to find such pairs for review, in line with the rule that examples are not truth.

## For Mova

- I1. Dictionary first, model second: look each transliterated token up in a table of forms already transcribed in the corpus, and call the neural or statistical model only for forms that stay ambiguous. The authors propose this themselves (section 6). In Mova it is a table lookup before any learned step, and it can be measured as coverage and accuracy on a held-out split.
- I4. Measure the end use, not only the token accuracy: run the analyser on the transcribed output and report lemma and POS recovery, as the paper does (recall about 90% at one candidate, 94% at three). Use the same two numbers for any Mova transcriber.

I1 and I3 go into `treebanks/akk`: the lookup table and the pair audit come before any model. I2 is a rule for the logogram lexicon in the same place. I4 is a measure for whatever transcriber is built there. None of the neural models is for Mova; the paper's numbers are the baseline to beat with tables.

## Institutions

[🎓**University of British Columbia** Vancouver · Canada 34 works · 32,862 citations](https://md.research.elamur.ai/free/en/universities/university-of-british-columbia/) · [🎓**University of Helsinki** Helsinki · Finland 24 works · 3,902 citations](https://md.research.elamur.ai/free/en/universities/university-of-helsinki/) · [🎓**University of Alberta** Edmonton · Canada 16 works · 2,099 citations](https://md.research.elamur.ai/free/en/universities/university-of-alberta/)

Topics[Akkadian and cuneiform NLP](https://md.research.elamur.ai/free/en/topics/akkadian-cuneiform-nlp/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

Cite as Aleksi Sahala, Miikka Silfverberg, Antti Arppe and Krister Lindén. 2020. Automated Phonological Transcription of Akkadian Cuneiform Text. Proceedings of the Twelfth Language Resources and Evaluation Conference.

The paper itself, not this card. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sahala2020automated,
  title = {{Automated Phonological Transcription of Akkadian Cuneiform Text}},
  author = {Aleksi Sahala, Miikka Silfverberg, Antti Arppe and Krister Lindén},
  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
  year = {2020},
  url = {https://aclanthology.org/2020.lrec-1.433/}
}
```

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## References

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