AraBit-1.58: Morphology-Aware Embeddings Reduce the Cost of Ternary Quantization in Arabic Text Simplification
Year: 2026
Type: article
Abstract: Extreme quantization restricts Transformer weights to the ternary set {−1,0,+1}, but whether this transfers to morphologically rich languages is untested. Arabic is demanding, because its clitic-heavy, root-and-pattern morphology yields a vast inventory of surface forms that subword tokenizers segment at linguistically meaningless boundaries. AraBit-1.58 couples a Morphology-Aware Tokenization and Embedding layer (MATE) with a BitNet b1.58 ternary backbone. MATE decomposes each word into proclitics, root, morphosemantic pattern, and enclitics, embeds these separately, and gates to a subword fallback when analysis fails. We evaluate on the SAMER Arabic simplification corpus under a controlled two-by-two design over three seeds that varies only the front end and the weight precision. Ternary quantization compresses weight memory 2.85×, from 104.72 to 36.70 MB, but without a fused ternary kernel it cuts neither latency nor energy. The comparison confirms our pre-specified hypothesis: quantization costs 9.43 SARI with subword input against 7.49 SARI with morphological input, an interaction of +1.93 SARI significant across seeds (paired t(2) =4.55, one-tailed p=0.023). Morphological grounding therefore buys robustness to extreme quantization rather than higher absolute quality, measured on SARI at small scale.
Author: Abdullah Alshanqiti
Institution: Islamic University of Madinah
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7224856440