Ancient Text Restoration
5 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Restoring and attributing ancient texts using deep neural networks
Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history.
Restoring ancient text using deep learning: a case study on Greek epigraphy
Ancient history relies on disciplines such as epigraphy, the study of ancient inscribed texts, for evidence of the recorded past.
Blank Language Models
We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks.
Probabilistically-sound beam search with masked language models
Beam search with masked language models (MLMs) is challenging in part because joint probability distributions over sequences are not readily available, unlike for autoregressive models.
Restoring Ancient Ideograph: A Multimodal Multitask Neural Network Approach
Cultural heritage serves as the enduring record of human thought and history.