R2T: Rule-Encoded Loss Functions for Sequence Tagging in Low-Resource Languages

arXiv:2510.13854 · cs.CL, cs.LG · Submitted 2025-10-12 · Read on arXiv

cs.CL, cs.LG

Submitted: 2025-10-12

Updated: 2026-09-27

Terminology

• Transformer — 35× in this paper · explained in 2 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Loss Function — 11× in this paper · explained in 2 episodes
• Core — 10× in this paper · explained in 2 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Regularization — 7× in this paper · explained in 3 episodes
• Hybrid Approach — 5× in this paper · explained in 4 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Low-Resource Languages — 4× in this paper · explained in 4 episodes
• Natural Language Processing — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• Transfer Learning — 3× in this paper · explained in 10 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• BiLSTM-CRF Out-of-Vocabulary — 1× in this paper
• Named Entity Recognition (NER) — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• TnT - A Statistical Part-of-Speech Tagger
• SMOL: Professionally translated parallel data for 115 under-represented languages
• Unsupervised Cross-lingual Representation Learning at Scale
• Bidirectional LSTM-CRF Models for Sequence Tagging
• Grammatical Error Correction for Low-Resource Languages: The Case of Zarma
• Regularizing Neural Networks by Penalizing Confident Output Distributions
• Data Programming: Creating Large Training Sets, Quickly
• Notes on Kullback-Leibler Divergence and Likelihood
• Attention Is All You Need

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