The multilingual neural machine translation (MNMT) aims for arbitrary translations across multiple languages.Although MNMT-specific models trained on parallel data offer low costs in training and deployment, their performance consistently lags behind that of large language models (LLMs).In this work, we introduce registering, a novel method that enables a small MNMT-specific model to compete with LLMs.Specifically, we insert a set of artificial tokens specifying the target language, called registers, into the input sequence between the source and target tokens.By modifying the attention mask, the target token generation only pays attention to the activation of registers, representing the source tokens in the target language space.Experiments on EC-40, a large-scale benchmark, show that our method advances the state-of-the-art of MNMT.We further pre-train two models, namely MITRE (multilingual translation with registers), by 9.3 billion sentence pairs across 24 languages collected from public corpora.One of them, MITRE-913M, outperforms NLLB-3.3B, achieves comparable performance with commercial LLMs, and shows strong adaptability in fine-tuning.Finally, we open-source our models to facilitate further research and development in MNMT: https://github.com/zhiqu22/mitre.

There has been a growing research interest in in-image machine translation, which involves translating texts in images from one language to another. Recent studies continue to explore pipeline-based systems due to its straightforward construction and the consistent improvement of its underlined components. However, the existing implementation for such pipeline often lack extensibility, composability, and support for real-time translation. Therefore, this work introduces ImageTra—an open-source toolkit designed to facilitate the development of the pipeline-based system of in-image machine translation. The toolkit integrates state-of-the-art open-source models and tools, and is designed with a focus on modularity and efficiency, making it particularly well-suited for real-time translation. The toolkit is released at https://github.com/hour/imagetra.

UDify is a multilingual and multi-task parser fine-tuned on mBERT that achieves remarkable performance in high-resource languages. However, the performance saturates early and decreases gradually in low-resource languages as training proceeds. This work applies a data augmentation method and conducts experiments on seven few-shot and four zero-shot languages. The unlabeled attachment scores were improved on the zero-shot languages dependency parsing tasks, with the average score rising from 67.1% to 68.7%. Meanwhile, dependency parsing tasks for high-resource languages and other tasks were hardly affected. Experimental results indicate the data augmentation method is effective for low-resource languages in a multilingual dependency parsing.