Zero Shot Translation

Zero-shot translation aims to enable machine translation between language pairs unseen during model training, significantly reducing the need for large parallel corpora for each language combination. Current research focuses on improving multilingual models, often leveraging transformer architectures, by employing techniques like decoupled vocabulary learning, disentangling semantic and linguistic features, and utilizing cross-lingual consistency regularization to enhance knowledge transfer and mitigate the "off-target" problem (incorrect language generation). These advancements hold significant potential for expanding machine translation capabilities to low-resource languages and facilitating cross-lingual communication in various applications, including text-to-image generation and multimodal translation.