Semantic Alignment across Ancient Egyptian Language Stages via Normalization-Aware Multitask Learning
This work addresses the problem of modeling historical languages with scarce parallel data for linguists and historians, but it is incremental as it builds on existing multitask learning approaches.
The researchers tackled semantic alignment across four historical stages of Ancient Egyptian by training a compact encoder-decoder model with multiple tasks and auxiliary views like Latin transliteration and IPA reconstruction. They found that translation yielded the strongest gains, with IPA improving cross-branch alignment, though overall alignment remained limited.
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. To reduce surface divergence we add Latin transliteration and IPA reconstruction as auxiliary views. We integrate these views through KL-based consistency and through embedding-level fusion. We evaluate alignment quality using pairwise metrics, specifically ROC-AUC and triplet accuracy, on curated Egyptian-English and intra-Egyptian cognate datasets. Translation yields the strongest gains. IPA with KL consistency improves cross-branch alignment, while early fusion demonstrates limited efficacy. Although the overall alignment remains limited, the findings provide a reproducible baseline and practical guidance for modeling historical languages under real constraints. They also show how normalization and task design shape what counts as alignment in typologically distant settings.