Evaluating Machine Translation Quality with Conformal Predictive Distributions

• URL: http://arxiv.org/abs/2306.01549v1
• Date: Fri, 2 Jun 2023 13:56:30 GMT
• Title: Evaluating Machine Translation Quality with Conformal Predictive Distributions
• Authors: Patrizio Giovannotti
• Abstract summary: We present a new approach for assessing uncertainty in machine translation by simultaneously evaluating translation quality and providing a reliable confidence score. Our method outperforms a simple, but effective baseline on six different language pairs in terms of coverage and sharpness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a new approach for assessing uncertainty in machine translation by simultaneously evaluating translation quality and providing a reliable confidence score. Our approach utilizes conformal predictive distributions to produce prediction intervals with guaranteed coverage, meaning that for any given significance level $\epsilon$, we can expect the true quality score of a translation to fall out of the interval at a rate of $1-\epsilon$. In this paper, we demonstrate how our method outperforms a simple, but effective baseline on six different language pairs in terms of coverage and sharpness. Furthermore, we validate that our approach requires the data exchangeability assumption to hold for optimal performance.

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