[R] Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation

[https://arxiv.org/abs/2402.19267](https://arxiv.org/abs/2402.19267)


Abstract : Employing extensive datasets enables the training of multilingual machine translation models; however, these models often fail to accurately translate sentences within specialized domains. Although obtaining and translating domain-specific data incurs high costs, it is inevitable for high-quality translations. Hence, finding the most 'effective' data with an unsupervised setting becomes a practical strategy for reducing labeling costs. Recent research indicates that this effective data could be found by selecting 'properly difficult data' based on its volume. This means …

abstract costs data datasets domain domains labeling machine machinelearning machine translation multilingual practical quality research strategy training translate translation unsupervised