Automatic metrics play a crucial role in machine translation. Despite the widespread use of n-gram-based metrics, there has been a recent surge in the development of pre-trained model-based metrics that focus on measuring sentence semantics. However, these neural metrics, while achieving higher correlations with human evaluations, are often considered to be black boxes with potential biases that are difficult to detect. In this study, we systematically analyze and compare various mainstream and cutting-edge automatic metrics from the perspective of their guidance for training machine translation systems. Through Minimum Risk Training (MRT), we find that certain metrics exhibit robustness defects, such as the presence of universal adversarial translations in BLEURT and BARTScore. In-depth analysis suggests two main causes of these robustness deficits: distribution biases in the training datasets, and the tendency of the metric paradigm. By incorporating token-level constraints, we enhance the robustness of evaluation metrics, which in turn leads to an improvement in the performance of machine translation systems. Codes are available at \url{https://github.com/powerpuffpomelo/fairseq_mrt}.

<iframe src="https://app.sli.do/event/oApzFnCrRo86e5qieE2QHQ/embed/polls/b39c8006-41d4-4da3-8538-6d05e701fb06" width="300" height="400"></iframe>

BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training

Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, current neural machine translation systems usually deliver unsatisfying translation results of dialogue texts. In this paper, we conduct a deep analysis of a dialogue corpus and summarize three major issues on dialogue translation. In response to these challenges, we propose a joint learning method to identify omission and typo in the process of translating and utilize context to translate dialogue utterances.

Autocorrect in the Process of Translation --- Multi-task Learning Improves Dialogue Machine Translation

Machine Translation 1

technical paper

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<i>**Session Chair: ** Young-Suk Lee </i><br><br>

IND3-Oral: Machine Translation (Industry Track)

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training

Autocorrect in the Process of Translation --- Multi-task Learning Improves Dialogue Machine Translation

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