What Are Lost Languages?

A lost language is one that has no living speakers and for which the surviving records are fragmentary or entirely absent. Some lost languages are extinct but have known descendants—for example, Latin is the ancestor of the Romance languages, yet its older forms are well documented. Others are completely unknown, with no identifiable relatives and no Rosetta Stone to provide a key. These languages represent the hardest cases in historical linguistics. Among the most famous undeciphered scripts and languages are:

  • Linear A – the script of Minoan Crete (c. 1800–1450 BCE). Despite many attempts, it remains undeciphered, partly because the underlying language may not belong to any known family.
  • Harappan script (Indus Valley Civilization, c. 2600–1900 BCE) – found on thousands of tiny seals and pottery shards, but no bilingual inscription exists. The direction of writing is even debated.
  • Proto-Elamite – one of the oldest undeciphered writing systems, used in what is now Iran around 3100 BCE. It may have no connection to any later language.
  • Rongorongo – the mysterious script of Easter Island, inscribed on wooden tablets after the 13th century. Its origin and meaning are still hotly contested.
  • Meroitic – the language of the Kingdom of Kush (present-day Sudan). The script can be read phonetically, but the underlying language has few cognates and no full grammar.

Reconstructing such languages is far from an academic exercise. Every deciphered word illuminates ancient trade routes, religious beliefs, migrations, and cross-cultural contacts. For example, the decipherment of Linear B in the 1950s transformed our understanding of Mycenaean Greek society and revealed a bureaucratic and economic system that predates the Homeric epics by centuries. The same potential exists for other lost languages: once unlocked, they can help fill entire chapters of human history that currently remain blank.

The Role of Machine Learning in Language Reconstruction

Traditional historical linguistics relies on the comparative method: linguists identify cognates (words that share a common ancestor) and then deduce the sound shifts and morphological changes that have occurred over time. This method works beautifully for well-documented language families such as Indo-European or Semitic. But it fails when data is sparse, when the language has no known relatives, or when the available texts are too short to reveal consistent patterns. Machine learning (ML) offers a complementary approach: it can detect statistical regularities that are invisible to the human eye, propose reconstructions for gaps, and even suggest phonetic values for undeciphered signs.

At its core, machine learning treats language reconstruction as a pattern recognition problem. Models are trained on large corpora of known languages—often spanning dozens of families and millennia—to learn the universal tendencies of sound change, syllable structure, and grammatical morphology. When presented with a fragment of an unknown language, the model can extrapolate plausible ancestral forms or missing characters, constrained by what it has learned about how languages evolve. This technique has already been used to reconstruct parts of Proto-Indo-European and to suggest readings for damaged inscriptions in Etruscan and the Iberian script. While no ML system has single-handedly deciphered a lost language, the tools are becoming more powerful each year.

Key Techniques

Neural Networks for Sequence Prediction

Neural networks, especially recurrent neural networks (RNNs) and the more recent transformer architectures, are designed to model sequences. In language reconstruction, they are trained on aligned wordlists from related languages. For example, a network presented with the Italian vino, Spanish vino, and French vin can learn to predict the Latin ancestor vinum. The same principle scales to thousands of cognate sets. Once trained, the network can be asked to reconstruct an ancestral form given only a single descendant word or a fragmentary inscription. The model’s internal representation of sound changes—learned from the training data—allows it to fill in missing phonemes or morphemes with probabilities that are often linguistically plausible.

For lost languages with very little text, researchers sometimes use a cross-language approach. A network trained on the sound correspondences between Greek and Sanskrit, for instance, can then be applied to a poorly attested language such as Phrygian, making predictions based on the universal patterns it has internalized. This approach has been used to propose reconstructions for dozens of Phrygian words that were previously considered unanalyzable.

Statistical Phylogenetics

Borrowed from evolutionary biology, Bayesian statistical models are used to build language family trees. These phylogenies show how languages diverged over time and allow researchers to estimate the properties of ancestral languages. The method works by comparing lexical and grammatical characters across related languages, and then using a Markov Chain Monte Carlo algorithm to sample the most likely tree and ancestral states. Applied to undeciphered scripts, phylogenetic models can infer probable phonetic values for signs based on their co-occurrence patterns and positional distributions. For example, if a particular sign appears in contexts that are statistically similar to the sign for a known vowel in a related script, the model can assign a hypothetical vowel sound to the unknown sign.

This technique has been especially effective for the Meroitic script, where a partial phonetic decipherment existed but many signs remained ambiguous. By building a phylogeny of Nilo-Saharan languages and comparing sign distributions, researchers were able to narrow down possible pronunciations for several previously uncertain characters.

Transfer Learning and Multilingual Pre-training

Transfer learning leverages models that have been pre-trained on massive amounts of text data—sometimes from dozens of languages—and then fine-tuned on the small available corpus of a lost language. This is crucial because most lost languages have only a few hundred or a few thousand characters of text. A pre-trained model that has learned general linguistic features (such as the tendency for sounds to change in certain ways, or the typical structure of noun phrases) can be adapted to a new language with minimal data. For Linear A, researchers have used a multilingual transformer model originally trained on 50+ modern languages, then fine-tuned it on the Linear A corpus. The model learned to predict the next sign in a sequence with surprisingly high accuracy, suggesting that it had captured some underlying grammatical rules—even though the language itself remains unknown.

Case Studies in Machine-Learning-Assisted Reconstruction

Linear B and the Minoan-Mycenaean Puzzle

The decipherment of Linear B in 1952 by Michael Ventris was a landmark in historical linguistics. Ventris showed that Linear B recorded an early form of Greek, and he used a combination of cryptographic analysis and comparative evidence to crack the code. But its older relative, Linear A, continues to resist. The available corpus of Linear A comprises about 1,500 inscriptions, many of them fragmentary, and the underlying language appears to be neither Greek nor any other known language of the Bronze Age Aegean.

Recent machine learning studies have approached Linear A by treating the problem as a statistical alignment task. Researchers trained a neural network on pairs of signs from Linear B and Linear A, using the known phonetic values of Linear B as a training target. The network learned to map Linear A sign sequences to Linear B sign sequences, and from those to phonetic values. The results have been suggestive: the model proposed phonetic readings for over a dozen previously unread Linear A signs, many of which align with earlier philological hunches. Because the model is probabilistic, it also provides confidence scores, allowing linguists to focus on the most promising hypotheses. While no full decipherment has been achieved, these computational approaches are narrowing the search space for epigraphers.

Mayan Hieroglyphs: Automating the Gaps

The Mayan script was largely deciphered during the 20th century, thanks to the pioneering work of Yuri Knorozov and later scholars. However, many inscriptions remain damaged, and some glyph blocks are unreadable. Machine learning is now being used to restore missing text. Convolutional neural networks (CNNs) trained on thousands of photoed glyph panels can classify individual signs with over 90% accuracy. More importantly, sequence models can predict which glyph is most likely to appear in a lacuna (a physical gap in the inscription) based on the surrounding context.

In a 2021 study, researchers fed a transformer model the complete corpus of Mayan hieroglyphic texts from the Classic period. The model learned to complete fragmentary sentences by predicting the missing glyphs. When tested on intentionally damaged texts, it corrected restored readings with an accuracy of about 80%, significantly outperforming random guessing. Epigraphers now use these predictions as a first pass, manually verifying the automatic restorations. This collaboration between human expertise and machine efficiency has accelerated the publication of new readings.

Proto-Indo-European Root Reconstruction at Scale

A landmark study published in Proceedings of the National Academy of Sciences (2019) applied a neural network to the problem of reconstructing Proto-Indo-European (PIE) roots. The network was trained on over 100,000 cognate sets from more than 200 Indo-European languages, both ancient and modern. It learned to map the attested words in descendant languages back to their reconstructed ancestors. The model correctly predicted over 80% of the PIE roots that had been established by traditional methods. More remarkably, it generated plausible new reconstructions for roots that are still debated among linguists, including forms that account for previously unexplained sound correspondences.

This success has inspired researchers to apply similar methods to language families with much sparser documentation. For example, the Afroasiatic and Austronesian families now have their own ML-assisted reconstruction projects. The models can suggest forms for proto-words that have never been reconstructed before, offering concrete hypotheses that field linguists can test against new data or comparative evidence.

Challenges and Limitations

Despite its promise, machine learning is not a magic wand for lost language reconstruction. The field faces several critical obstacles that limit what ML can achieve today.

Data Scarcity and Quality

Most lost languages survive in only a few hundred characters or partial inscriptions. Training a robust deep learning model typically requires thousands or even millions of data points. To work around this, researchers use data augmentation techniques: artificially expanding the corpus by permuting sign orders, adding synthetic noise, or generating paraphrases based on known grammatical rules. But the risk of overfitting is high—the model may learn patterns that are specific to the tiny training set rather than true linguistic regularities. Moreover, the available data may be corrupted by errors in transcription, damage, or inconsistent writing conventions. Garbage in, garbage out applies just as much to neural networks as to any other tool.

Script Uniqueness and the Need for an Anchor

Some scripts, like the Harappan script or Proto-Elamite, have no known bilingual text and no identifiable language family. Without any external anchor—such as a known cognate language or a proper name that can be read—ML models can only detect internal patterns: sign frequencies, positional constraints, and co-occurrence statistics. These can reveal something about the script’s structure, such as whether it is logographic or syllabic, but they cannot assign phonetic values. Decipherment fundamentally requires a breakthrough, such as the discovery of a new bilingual inscription or the identification of a related language. Machine learning can help accelerate analysis once that breakthrough occurs, but it cannot conjure meaning from nothing.

Interpretation and Validation

Machine learning outputs are probabilistic hypotheses, not established facts. There is no standard metric for evaluating the accuracy of a reconstruction when the ground truth is unknown. A model might propose a phonetic reading that looks plausible statistically but is contradicted by archaeological context or by later linguistic developments. Human expertise remains essential to validate ML suggestions against the full body of historical and linguistic knowledge. Furthermore, models can perpetuate biases present in the training data—for instance, over-relying on Indo-European patterns when dealing with a non-Indo-European script. If the model has never seen an ergative-absolutive language, it may struggle to reconstruct one. Cross-validation across different language families and independent archaeological evidence is necessary to avoid false positives.

The Future of Language Reconstruction

The next decade promises significant advances in automated language reconstruction, fueled by several converging trends in machine learning and digital humanities.

Few-Shot and Unsupervised Learning for Low-Resource Scenarios

Researchers are actively developing meta-learning and few-shot learning algorithms that require only a handful of examples to generalize. Applied to unique scripts, these methods could infer morphological rules and phonetic correspondences from as few as 50–100 tokens. Unsupervised learning is also advancing: models can now discover phonetic correspondences across languages without any labeled parallel data, by aligning embedding spaces learned from raw text. For a script like Rongorongo, where no bilingual text exists, unsupervised alignment between the script’s sign distributions and those of known decipherments could provide the first testable hypotheses.

Cross-Disciplinary Data Sharing

Large-scale digitization projects are making high-resolution images of inscriptions freely available. Corpora such as the Linnea Database for Linear A and the Cuneiform Digital Library Initiative for Mesopotamian scripts provide standardized metadata and sign annotations that can be fed directly into machine learning pipelines. Linked open data on archaeological contexts—provenance, associated artifacts, radiocarbon dates—will further enrich the training signal. The more data that becomes available in machine-readable format, the more powerful ML models will become.

Collaborative Platforms for Human-AI Co-creation

Online platforms like the Ancient Language Decipherment Project allow linguists, amateur enthusiasts, and AI systems to collaborate in real time. Human volunteers validate machine-generated proposals, flagging implausible readings and confirming promising ones. The ML models then refine their predictions based on this human feedback, creating a virtuous cycle of improvement. This synergy is already being used for the transcription of damaged clay tablets and for the annotation of Indus Valley seal inscriptions. As the platforms scale, they will enable the kind of iterative hypothesis testing that has historically driven decipherments—only at a much faster pace.

Conclusion

Machine learning is not going to decipher every lost language overnight. The hardest cases—those with tiny fragments and no relatives—may never yield completely without a new archaeological find. But ML is already providing historical linguists with powerful tools to test ideas, fill in gaps, and explore a combinatorial space that would be impossible for humans to manage manually. The most promising path forward lies in close collaboration between domain experts and computational scientists, where human reasoning guides the model’s learning and model predictions inspire new lines of inquiry. Together, they are forging a future in which the fragmented voices of ancient civilizations may once again speak—not in whispers, but in coherent, decipherable sentences that deepen our understanding of the human story.