用回归方法微调BERT，提升希伯来圣经文本复用检测能力
MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection
- 基于余弦相似度回归，微调AlephBERT模型学习经文语义空间
- 叙事类平行文本召回率达87.1%，重叠区域从24%降至6%
- 适合研究圣经叙事文本复用，对诗歌体较难适用
希伯来圣经中广泛存在文本复用现象，但现有计算方法仍依赖词汇重合，面对改写、同义替换或句法重构时表现不佳。本文提出MiqraBERT，一个在AlephBERT基础上微调的Sentence-BERT模型，用于圣经希伯来语的逐节语义相似性分析。训练集包含1,650对标注的经文与半节经文：825对真实平行（来自列王纪互见材料及诗体平行研究）与825对随机负样本。通过余弦相似度回归，模型学习使平行经文聚类、无关经文分离的嵌入空间。使用分布型指标（瓦瑟斯坦距离、重叠系数）在十次随机种子下评估分离效果，MiqraBERT相较预训练基线将分布分离能力提升2.7倍，模糊重叠区从约24%降至约6%。叙事互见文本召回@10达87.1%；诗体平行仍低于9%。这种文体差异限制了模型在叙事类文本复用中的可靠应用。MiqraBERT已在Hugging Face公开：https://huggingface.co/davidmsmiley/MiqraBERT
原文摘要 · Abstract (English)
Textual reuse pervades the Hebrew Bible, yet the computational methods used to detect it still rest largely on lexical overlap, and they falter once a parallel involves paraphrase, lexical substitution, or syntactic reworking. This paper introduces MiqraBERT, a Sentence-BERT model finetuned from AlephBERT (a Modern Hebrew encoder) for verse-level semantic similarity in Biblical Hebrew. The training set comprises 1,650 labeled verse and half-verse pairs: 825 true parallels drawn from the Chronicles synoptic material and from foundational studies of poetic parallelism, balanced against 825 randomly sampled negatives. Through cosine-similarity regression, the model learns an embedding space in which parallel verses cluster together and unrelated verses move apart. We evaluate separation with distribution-based metrics, Wasserstein distance and the overlap coefficient, across ten random seeds. MiqraBERT improves distributional separation 2.7-fold over the pre-trained baseline and reduces the ambiguous overlap region from roughly 24% to about 6%. Narrative synoptic parallels reach a recall@10 of 87.1%; poetic parallels remain difficult, below 9%. This genre-dependent asymmetry confines the model's reliable scope to narrative textual reuse. MiqraBERT is publicly available at https://huggingface.co/davidmsmiley/MiqraBERT
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