Papers with embedding-based neural text evaluation metrics
Hacking Neural Evaluation Metrics with Single Hub Text (2026.eacl-short)
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| Challenge: | Recent embedding-based neural text evaluation metrics are not reliable due to black-box nature of neural networks. |
| Approach: | They propose to find a single adversarial text in the discrete space that is consistently evaluated as high-quality regardless of the test cases. |
| Outcome: | The proposed method outperforms translations generated individually for each source sentence in English-to-Japanese and English- to-German translation tasks. |