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

arXiv:2402.19267v1 Announce Type: new
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. …

abstract arxiv costs cs.ai cs.cl data datasets domain domains guidance machine machine translation multilingual quality robust training translate translation type unsupervised