用词典增强图模型预测阿拉伯语文档可读性
Lexicon-Enriched Graph Modeling for Arabic Document Readability Prediction
- 构建句子级图结构，融合词典与上下文嵌入特征
- 混合模型在文档可读性预测上超越单一模型
- 适合需要精准评估文本难度的研究者使用
我们提出一种基于图的阿拉伯语文档可读性预测方法，作为2025年BAREC共享任务约束赛道的一部分。将每个文档建模为句子级图，节点代表句子和词干，边表示词汇共现与类别归属等语言关系。句子节点融合SAMER词典特征与阿拉伯语Transformer的上下文嵌入。图神经网络（GNN）与Transformer编码器作为独立分支训练，推理时通过后期融合结合预测结果。文档级预测采用最大池化聚合句级输出，反映最困难句子。实验表明，该混合方法在多个可读性指标上优于单独的GNN或Transformer分支。总体发现，融合策略在文档级预测中具优势，但仅用GNN在句级精确预测上表现更佳。
原文摘要 · Abstract (English)
We present a graph-based approach enriched with lexicons to predict document-level readability in Arabic, developed as part of the Constrained Track of the BAREC Shared Task 2025. Our system models each document as a sentence-level graph, where nodes represent sentences and lemmas, and edges capture linguistic relationships such as lexical co-occurrence and class membership. Sentence nodes are enriched with features from the SAMER lexicon as well as contextual embeddings from the Arabic transformer model. The graph neural network (GNN) and transformer sentence encoder are trained as two independent branches, and their predictions are combined via late fusion at inference. For document-level prediction, sentence-level outputs are aggregated using max pooling to reflect the most difficult sentence. Experimental results show that this hybrid method outperforms standalone GNN or transformer branches across multiple readability metrics. Overall, the findings highlight that fusion offers advantages at the document level, but the GNN-only approach remains stronger for precise prediction of sentence-level readability.
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