Enabling Unsupervised Neural Machine Translation with Word-level Visual Representations.

Chengpeng Fu,Xiaocheng Feng,Yichong Huang, Wenshuai Huo, Hui Wang,Bing Qin,Ting Liu

EMNLP 2023(2023)

引用 0|浏览7
暂无评分
摘要
Unsupervised neural machine translation has recently made remarkable strides, achieving impressive results with the exclusive use of monolingual corpora. Nonetheless, these methods still exhibit fundamental flaws, such as confusing similar words. A straightforward remedy to rectify this drawback is to employ bilingual dictionaries, however, high-quality bilingual dictionaries can be costly to obtain. To overcome this limitation, we propose a method that incorporates images at the word level to augment the lexical mappings. Specifically, our method inserts visual representations into the model, modifying the corresponding embedding layer information. Besides, a visible matrix is adopted to isolate the impact of images on other unrelated words. Experiments on the Multi30k dataset with over 300,000 self-collected images validate the effectiveness in generating more accurate word translation, achieving an improvement of up to $+$2.81 BLEU score, which is comparable or even superior to using bilingual dictionaries.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要