Test-time Adaptation for Machine Translation Evaluation by Uncertainty Minimization (2023.acl-long)
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| Challenge: | evaluators of machine translation systems often use text-based metrics to evaluate performance . however, these metrics lack semantic-level information and exhibit poor correlation with human ratings . authors propose a method to reduce inference bias of neural metrics in out-of-distribution data . |
| Approach: | They propose to reduce inference bias by using uncertainty estimation, test-time adaptation, and inference to reduce model uncertainty. |
| Outcome: | The proposed method reduces model uncertainty and improves correlation performance across models. |
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| Challenge: | Trainable evaluation metrics for machine translation (MT) exhibit strong correlation with human judgements, but they are often hard to interpret and might produce unreliable scores under noisy or out-of-domain data. |
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Uncertainty-Aware Machine Translation Evaluation (2021.findings-emnlp)
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| Challenge: | Several neural-based metrics have been proposed to evaluate machine translation quality, but they are trained on noisy, biased and scarce human judgements. |
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| Challenge: | Current evaluation methods focus on one dataset, e.g., Newstest dataset in each year’s WMT Metrics Shared Task. |
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Evaluating Automatic Metrics with Incremental Machine Translation Systems (2024.findings-emnlp)
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| Challenge: | Existing studies have shown that neural metrics are more reliable than non-neural metrics. |
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| Challenge: | Uncertainty estimation is an important diagnostic tool for statistical models. |
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| Challenge: | Neural Machine Translation models are effective when trained on broad domains with large datasets, such as news translation. |
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Difficulty-Aware Machine Translation Evaluation (2021.acl-short)
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| Challenge: | Current MT evaluation measures pay the same attention to each sentence component . in real-world examinations, the questions vary in difficulty and weightings . |
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Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort (2021.acl-long)
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| Challenge: | Existing methods to evaluate multiple systems are expensive and require human evaluators. |
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Non-Autoregressive Neural Machine Translation: A Call for Clarity (2022.emnlp-main)
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| Challenge: | Non-autoregressive translation models require a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
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Quality-Aware Decoding for Neural Machine Translation (2022.naacl-main)
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Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, Andre Martins
| Challenge: | Despite advances in machine translation quality estimation and evaluation, decoding is mostly oblivious to this. |
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