Papers with SacreBLEU

2 papers
Translation Canvas: An Explainable Interface to Pinpoint and Analyze Translation Systems (2024.emnlp-demo)

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Challenge: Existing tools for evaluation of translation models focus on high-level metrics like BLEU or COMET scores, which are time-consuming and prone to error.
Approach: They propose a toolkit that provides a detailed analysis of translation models and a user-friendly interface.
Outcome: The toolkit shows superior performance over COMET and SacreBLEU packages under enjoybility and understandbility criteria.
Prediction Difference Regularization against Perturbation for Neural Machine Translation (2022.acl-long)

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Challenge: Existing methods for regularizing input perturbation are limited by under-fitting of training data.
Approach: They propose a method that can reduce over-fitting and under-fitting at the same time.
Outcome: The proposed method can reduce over-fitting and under-fitturing while making the model less sensitive to small input changes and more robust to under-perturbed training data.

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