Papers by Nelson Marcos

1 papers
Finding the Optimal Byte-Pair Encoding Merge Operations for Neural Machine Translation in a Low-Resource Setting (2024.findings-emnlp)

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Challenge: Using different byte pair encoder configurations, we can improve neural machine translation performance for low-resource languages.
Approach: They investigate the impact of different Byte Pair Encoding configurations on neural machine translation performance for the Filipino-Cebuano language pair across various text domains.
Outcome: The proposed methods show that smaller BPE configurations yield higher BLEU scores, indicating improved translation quality through finer tokenization granularity . larger BPE setups and the absence of BPE result in lower BLUE scores, suggesting a decline in translation quality due to coarser tokenisation.

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