An automatic model and Gold Standard for translation alignment of Ancient Greek

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Yousef, Tariq
Palladino, Chiara
Shamsian, Farnoosh
D'Orange Ferreira, Anise [UNESP]
dos Reis, Michel Ferreira [UNESP]

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This paper illustrates a workflow for developing and evaluating automatic translation alignment models for Ancient Greek. We designed an annotation Style Guide and a gold standard for the alignment of Ancient Greek-English and Ancient Greek-Portuguese, measured inter-annotator agreement and used the resulting dataset to evaluate the performance of various translation alignment models. We proposed a fine-tuning strategy that employs unsupervised training with mono- and bilingual texts and supervised training using manually aligned sentences. The results indicate that the fine-tuned model based on XLM-Roberta is superior in performance, and it achieved good results on language pairs that were not part of the training data.



Alignment Guidelines, Ancient Greek, Gold Standard, Translation Alignment, Alignment guideline, Ancient Greeks, Automatic modeling, Automatic translation, Fine tuning, Gold standards, Performance, Style guides, Translation alignment, Work-flows

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2022 Language Resources and Evaluation Conference, LREC 2022, p. 5894-5905.