AI system
- System
-
Akkadian-to-English neural machine translation (C2E and T2E models)
- Model
-
A convolutional model implemented in Fairseq.
- Release
-
Exact release not established.
- Application developer
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Tel Aviv University (Gutherz, Levy, Berant) and Ariel University (Gordin)
- Role
-
The models translated Akkadian text into English.
- Configuration
-
Researchers used a convolutional model implemented in Fairseq.
- Inputs and outputs
-
Not assessed
- Autonomy
-
Not assessed
- Workflow
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Convolutional neural network translation models trained on digitized Akkadian-English pairs.
- Human oversight
-
Not assessed
- Relation to AI
-
The models' translation quality is reported by their developers in a peer-reviewed paper. No scholar-accepted translation of a specific text is attributed to them. The authors note limits on longer and fragmented texts.
- AI contribution
-
The models' translation quality is reported by their developers in a peer-reviewed paper. No scholar-accepted translation of a specific text is attributed to them. The authors note limits on longer and fragmented texts.
Benefits
- Outcome identifiers
-
Not assessed
- Benefit
-
BLEU4 scores of 36.52 and 37.47
- Category
-
Improved Task Accuracy
- Status
-
Not assessed
- Beneficiary groups
-
People. Organizations. Society.
- Beneficiaries
-
Not assessed
- Reach
-
Not assessed
- Distribution and access
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Not assessed
- Duration
-
Not assessed
- Expected?
-
Yes
- Costs and harms
-
Not assessed
Measurement
- Studies
-
Not assessed
- Study design
-
Not assessed
- Measurement
-
Not assessed
- Setting and sample
-
Not assessed
- Measures
-
BLEU4 scores of 36.52 and 37.47
- Comparison
-
a translation memory baseline on the same test data
- Results
-
Reference: the translation memory baseline achieved a BLEU4 score of 23.51. Result: 36.52 and 37.47 Best Bilingual Evaluation Understudy 4 (BLEU4) scores for C2E and T2E, respectively.
- Additional value from AI
-
Not assessed
- Limitations
-
Not assessed
- Denominators
-
Not assessed
- Materials for replication
-
Not assessed
Money
- Kind of value
-
Not assessed
- Amount
-
Not assessed
- Currency and prices
-
Not assessed
- Period and unit
-
Not assessed
- Realization
-
Not assessed
- Recipients and payers
-
Not assessed
- Gross or net
-
Not assessed
- Included costs
-
Not assessed
- Comparison
-
Not assessed
- Method
-
Not assessed
- Return and payback
-
Not assessed
People and places
- Participants
-
Developer: Tel Aviv University (Gutherz, Levy, Berant) and Ariel University (Gordin). Operator: None (research demonstration).
- Jurisdictions
-
Benefit:
Israel. Developer: Israel. - Ownership and control
-
Not assessed
- Dependencies
-
Not assessed
- Cross-border connections
-
Not assessed
- Geopolitical implications
-
Not assessed
Follow-up
- Conditions for replication
-
Human scholars correct outputs, especially for long or fragmentary texts.
- Replication attempts
-
1 follow-up entries. Benefit recurred: 1.
- Later findings
-
14 May 2026. The approach was built upon rather than contradicted: a May 2026 arXiv preprint by Zhaohui Wang (USC) cites the 2023 system and reports a bidirectional ByT5-based model reaching 49.1 BLEU for Akkadian-to-English on the same Akkademia validation split; no later report from the Tel Aviv and Ariel group itself was found.
- Availability
-
Not assessed
- Institutional decision
-
Not assessed
- Grounds
-
Not assessed
- Action and result
-
Not assessed
- Success criterion
-
Not assessed
Evidence
- Source numbers
-
AIBD-0277-source-0001–0003
- Citations
-
Numbered references below.
- Language
-
Not assessed
- Source relationships
-
Developer, Independent
- Original evidence
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Not assessed
- Access and versions
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Not assessed
- Supported findings
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Not assessed
- Evidence links
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Not assessed
- Evidence strength
-
Not assessed
- Missing evidence
-
Not assessed
Grades
- Assessed benefits
-
People. Organizations. Society.
- Depth
-
Not assessed
- Reasons
-
People: No individual beneficiary. Organizations: No deployment reported. Society: No attributable event.
- Observed or intended
-
Not assessed
- Rubric
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Not assessed
- Assessor and date
-
Not assessed
- Alternative assessments
-
Not assessed
- Fill
-
Independently reported
Entry details
- Kind
-
Not assessed
- Related entries
-
Not assessed
- Field
-
Digital humanities; Assyriology. Assyriologists and interested lay readers.
- Purpose
-
Automatically translate Akkadian cuneiform texts into English to assist scholars
- Dates
-
Date basis unassessed: 2023 (precision unassessed). Updated: 21 October 2026.
- Stage
-
Not assessed
- Version
-
SHA-256: 48b8e8010ae6…
References
- Other identifiers
-
Not assessed
- Category mappings
-
Not assessed
- Brief
-
Not assessed
- Revisions
-
6 revisions. Latest: 21 October 2026.
Sources
-
Peer-reviewed study Developer
Gutherz G, Gordin S, Saenz L, Levy O, Berant J. PNAS Nexus 2(5):pgad096 (2023)
we get 36.52 and 37.47 Best Bilingual Evaluation Understudy 4 (BLEU4) scores for C2E and T2E, respectively
-
Peer-reviewed study Developer
Gordin S, Gutherz G, Elazary A, et al. PLoS ONE 15(10):e0240511 (2020)
with an accuracy reaching up to 97% for automatically transliterating and segmenting standard Unicode cuneiform glyphs into words.
-
Preprint Independent author
Wang Z (14 May 2026). TabletCraft: Bridging a 4,000-Year Cultural Gap with Bidirectional Akkadian NMT and Cuneiform Rendering. arXiv:2608.02609.