AraBERT-based framework reaches 96.88% accuracy on Arabic DP ambiguity
The study introduces a generatively informed neuro‑symbolic framework that integrates AraBERT to resolve structural ambiguity in Modern Standard Arabic noun phrases. By treating ambiguity as a candidate‑based decision task, the model explicitly constructs and evaluates linguistically motivated alternatives. On an unseen evaluation set, it achieved 96.88 % accuracy, 95.92 % macro‑F1, 96.83 %…
Key points
- Model achieved 96.88% accuracy on unseen Arabic DP set
- Recall 99.71% for High/VP Attachment, 89.26% for Low/NP/Embedded Attachment
- Framework combines generative syntax with AraBERT in a candidate‑based task
Class‑level analysis shows uneven performance: recall of 99.71 % for High/VP Attachment (N1) versus 89.26 % for Low/NP/Embedded Attachment (N2), indicating that embedded interpretations are harder to recover. The authors argue that formal syntactic representations can be operationalized within Transformer‑based NLP, offering a controlled and interpretable approach to Arabic syntactic ambiguity resolution and potentially beyond.
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