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Learning from Many Voices: Literary MT Using Multi-Reference Human and Synthetic Data
Multiple valid translations of a single literary work naturally exist. We investigate strategies for leveraging these multi-reference datasets to improve literary machine translation. We propose a filtering framework based on semantic similarity to identify source texts whose references display mean
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