AI RESEARCH
Semantic Alignment across Ancient Egyptian Language Stages via Normalization-Aware Multitask Learning
arXiv CS.CL
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ArXi:2603.24258v1 Announce Type: new We study word-level semantic alignment across four historical stages of Ancient Egyptian. These stages differ in script and orthography, and parallel data are scarce. We jointly train a compact encoder-decoder model with a shared byte-level tokenizer on all four stages, combining masked language modeling (MLM), translation language modeling (TLM), sequence-to-sequence translation, and part-of-speech tagging under a task-aware loss with fixed weights and uncertainty-based scaling.