H-Net++: Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages

• URL: http://arxiv.org/abs/2508.05628v1
• Date: Thu, 07 Aug 2025 17:59:01 GMT
• Title: H-Net++: Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages
• Authors: Mehrdad Zakershahrak, Samira Ghodratnama,
• Abstract summary: H-NET++ is a hierarchical dynamic-chunking model that learns linguistically-informed segmentation through end-to-end training.<n>On a 1.4B-token Persian corpus, H-NET++ achieves state-of-the-art results.
• Score: 0.6629765271909505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Byte-level language models eliminate fragile tokenizers but face computational challenges in morphologically-rich languages (MRLs), where words span many bytes. We propose H-NET++, a hierarchical dynamic-chunking model that learns linguistically-informed segmentation through end-to-end training. Key innovations include: (1) a lightweight Transformer context-mixer (1.9M parameters) for cross-chunk attention, (2) a two-level latent hyper-prior for document-level consistency, (3) specialized handling of orthographic artifacts (e.g. Persian ZWNJ), and (4) curriculum-based training with staged sequence lengths. On a 1.4B-token Persian corpus, H-NET++ achieves state-of-the-art results: 0.159 BPB reduction versus BPE-based GPT-2-fa (12% better compression), 5.4pp gain on ParsGLUE, 53% improved robustness to ZWNJ corruption, and 73.8% F1 on gold morphological boundaries. Our learned chunks align with Persian morphology without explicit supervision, demonstrating that hierarchical dynamic chunking provides an effective tokenizer-free solution for MRLs while maintaining computational efficiency.

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