Papers with BLASER 2.0

1 papers
BLASER 2.0: a metric for evaluation and quality estimation of massively multilingual speech and text translation (2024.findings-emnlp)

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Challenge: Automatic evaluation of machine translation (MT) is difficult because of the number of possible ways to express a thought in a language.
Approach: They propose to use BLASER 2.0 to evaluate machine translation quality . they propose to apply the reference-based model to a sentence-based version .
Outcome: The proposed model is applicable to detecting translation hallucinations and filtering training datasets to obtain more reliable translation models.

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