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

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Data

2022-01-01

Autores

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

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Resumo

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.

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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

Como citar

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

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