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.

本文提出一种跨媒体对比学习方法，将几种低资源语言与高资源语言相连，通过少量的图像 - 文本对实现神经机器翻译，取得了显著的效果。

跨模态对齐的低资源神经机器翻译

Low-resource Neural Machine Translation with Cross-modal Alignment

A number of cross-lingual transfer learning approaches based on neural
networks have been proposed for the case when large amounts of parallel text
are at our disposal. However, in many real-world settings, the size of parallel
annotated training data is restricted. Additionally, prior cross-lingual
mapping research has mainly focused on the word level. This raises the question
of whether such techniques can also be applied to effortlessly obtain
cross-lingually aligned sentence representations. To this end, we propose an
Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which
learns mappings of cross-lingual sentence representations from limited
quantities of parallel data.

提出了 Adversarial Bi-directional Sentence Embedding Mapping（ABSent）框架，它能够从有限量的平行数据中学习跨语言句子表示的映射，解决了跨语言转移学习中平行数据量有限的问题。