The study investigates whether morphological correctness influences tokenization effectiveness in Tagalog, a language with infix morphology that disrupts contiguous roots. Researchers developed MorphAustro‑Tok, a tokenizer that preserves the root as a contiguous unit and separates infixes, and compared it to a standard BPE baseline and a scrambled‑morpheme control that randomizes infix placement. Across multiple runs, MorphAustro‑Tok consistently outperformed the baseline, but the scrambled control showed no significant difference, indicating that morphological correctness does not contribute to the observed gains at this scale.
By Abdul Rehman Antall, Baidya Nath Saha
The paper presents an end‑to‑end sequence‑to‑sequence approach for correcting Tamil spelling and grammar errors, leveraging progressively fine‑tuned transformer models (mT5‑small and mBART‑50). Using a synthetic corpus of 657,720 noisy‑clean sentence pairs across ten error categories, the authors introduce a four‑stage training schedule that targets surface noise, contextual grammar, single‑site sandhi, and multi‑site cross‑word sandhi. The best model, mBART‑50 v5, achieves 69.3% exact‑match accuracy on a balanced diagnostic set, with notable gains in sandhi (87.5%) and subject‑verb agreement (43.5%) accuracy, while also revealing a precision‑recall trade‑off for sandhi corrections.
By Karthikeyan A, Jaya Nirmala S, Sangeetha Sivanesan, Indhu R, Pranav Kumar, Bharat Jude Johnson, Vishnu Ram
arXiv:2608.09289v2 Announce Type: replace
Abstract: Second language writing research distinguishes grammatical accuracy from native-like idiomaticity, yet automated writing evaluation often conflates...
By Steve Woollaston, Brendan Flanagan, Hiroaki Ogata
MoVoC introduces a morphology-aware subword construction method for Ge'ez script languages, integrating supervised morphological analysis with Byte Pair Encoding to preserve morphological boundaries while retaining lexical meaning. The resulting MoVoC-Tok tokenizer outperforms baselines in MorphScore and morpheme boundary precision across several low-resource, morphologically complex languages, notably achieving significant gains on Tigre through cross-lingual application. The authors also release morpheme-annotated datasets and a morpheme-aware vocabulary for public use.
By Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl
arXiv:2606. 15144v1 Announce Type: cross Abstract: Large language models (LLMs) process text as sequences of subword tokens, which can obscure the character-level and morphological structure that underlies word formation.
By Jann Railey Montalan, David Demitri Africa, Jimson Paulo Layacan, Richell Isaiah Flores, Ivan Yuri De Leon, Lance Calvin Gamboa
The paper introduces a compact BART-based sequence‑to‑sequence model for low‑latency spell correction of Japanese music search queries, addressing challenges posed by four writing scripts. It employs a script‑aware synthetic misspelling pipeline that blends keyboard‑layout models, phonetic confusion priors, consonant alternations, and kana case errors, and normalizes mixed‑script titles to a single canonical script to reduce hallucinations. Experiments show the model achieves 41.09% exact‑match accuracy and 11.62% CER, outperforming edit‑distance baselines while keeping inference latency under 4 ms on a single GPU.
By Anshul Garg, Pavni Tandon, Karan Bhukar, Tanmay Khandelwal, Ujjal Kumar Dutta