Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains (2024.acl-short)
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| Challenge: | a new dataset examines whether fine-tuned metrics are robust to domain shifts between training and inference. |
| Approach: | They use an annotated multidimensional quality metrics dataset to examine whether they are robust to domain shifts between training and inference. |
| Outcome: | The proposed metrics exhibit a substantial performance drop in the unseen domain scenario compared to metrics that rely on the surface form and pre-trained metrics that are not fine-tuned on MT quality judgments. |
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