CWoMP: Morpheme Representation Learning for Interlinear Glossing

arXiv:2603.18184 · cs.CL · Submitted 2026-03-18 · Read on arXiv

cs.CL

Submitted: 2026-03-18

Updated: 2026-09-01

Project page: https://cwomp.github.io

Terminology

• Embeddings — 26× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• RAG — 5× in this paper · explained in 3 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Tokenization — 5× in this paper · explained in 2 episodes
• Morpheme Error Rate — 4× in this paper
• Adam — 3× in this paper · explained in 2 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Low-Resource Languages — 3× in this paper · explained in 4 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• F1 Score — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Generative Recommendation — 1× in this paper · explained in 2 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• SIGMORPHON 2023 Shared Task of Interlinear Glossing: Baseline Model
• GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text
• Massively Multilingual Joint Segmentation and Glossing
• Efficient Estimation of Word Representations in Vector Space
• A Benchmark for Learning to Translate a New Language from One Grammar Book
• Representation Learning with Contrastive Predictive Coding
• Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
• ByT5: Towards a token-free future with pre-trained byte-to-byte models
• A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation
• Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions

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