2026
EACL
EACL 2026
Leveraging CoHere Multilingual Embeddings and Inverted Softmax Retrieval for Automatic Parallel Sentence Alignment in Low-Resource Languages
Abstract
AbstractWe present an improved method for automaticparallel sentence alignment in low- resourcelanguages. We used CoHere multilingualembeddings and inverted softmax retrieval.Our technique achieved a higher F1-score of78.30% on the MAFAND-MT test set, comparedto the existing technique’s 54.75%. Precisionand recall have shown similar performance.We assessed the quality of the extracted data bydemonstrating that it outperforms the existingtechnique in terms of low-resource translationperformance.
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio