跨古埃及语四个阶段的语义对齐，提升历史语言模型性能。
Semantic Alignment across Ancient Egyptian Language Stages via Normalization-Aware Multitask Learning
- 多任务联合训练，融合音译与音标辅助视图增强对齐。
- 音标结合KL一致性使跨语支对齐提升，翻译任务收益最大。
- 为稀疏数据下的历史语言建模提供可复现基线与设计指南。
我们研究了古埃及语四个历史阶段之间的词级语义对齐问题。这些阶段在书写系统和正字法上差异显著，平行数据稀缺。我们联合训练一个紧凑的编码器-解码器模型，使用共享的字节级分词器，在所有四个阶段上同时执行掩码语言建模（MLM）、翻译语言建模（TLM）、序列到序列翻译及词性标注任务，采用任务感知损失并固定权重与基于不确定性的缩放机制。为减少表面差异，引入拉丁转写和IPA音标重构作为辅助视图，并通过KL一致性与嵌入层融合进行整合。使用精心构建的埃及语-英语及内部同源词数据集，通过成对评估指标（如ROC-AUC与三元组准确率）衡量对齐质量。结果表明，翻译任务带来最强增益；结合KL一致性的IPA视图显著改善跨分支对齐效果，而早期融合则作用有限。尽管整体对齐仍受限，但本研究提供了可复现的基线，并为真实条件下历史语言建模提供实用指导，揭示了归一化与任务设计如何影响形态差异大的语境中对齐的定义。
原文摘要 · Abstract (English)
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.
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