论文标题
低源神经机器翻译与跨模式对齐
Low-resource Neural Machine Translation with Cross-modal Alignment
论文作者
论文摘要
如何使用有限的并行数据实现神经机器翻译？现有的技术通常依赖于大规模的单语言语料库，这对于某些低资源语言是不切实际的。在本文中，我们转向通过其他视觉方式将几种低资源语言连接到一种特定的高资源。具体而言，我们提出了一种跨模式的对比学习方法，以学习所有语言的共享空间，在该方法中均介绍了粗粒度的句子级别的目标和细粒度的令牌级别。实验结果和进一步的分析表明，我们的方法可以通过少量的图像文本对有效地学习跨模式和跨语言对准，并在零射击和少数拍摄的情况下对仅文本基线进行显着改善。
How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpora, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality. Specifically, we propose a cross-modal contrastive learning method to learn a shared space for all languages, where both a coarse-grained sentence-level objective and a fine-grained token-level one are introduced. Experimental results and further analysis show that our method can effectively learn the cross-modal and cross-lingual alignment with a small amount of image-text pairs and achieves significant improvements over the text-only baseline under both zero-shot and few-shot scenarios.
