Description

A new study by researchers from Google and Imperial College London challenges a core assumption in AI translation evaluation: that a single metric can capture both semantic accuracy and naturalness of translations.
“Single-score summaries do not and cannot give the complete picture of a system’s true performance,” the researchers said.
In the latest WMT general task, they observed that systems with the best automatic scores — based on neural metrics — did not receive the highest scores from human raters. “This and related phenomena motivated us to reexamine translation evaluation practices,”

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