ZhenHao Tang


2024

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Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets
Zi Long | ZhenHao Tang | Xianghua Fu | Jian Chen | Shilong Hou | Jinze Lyu
Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024

2022

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Multimodal Neural Machine Translation with Search Engine Based Image Retrieval
ZhenHao Tang | XiaoBing Zhang | Zi Long | XiangHua Fu
Proceedings of the 9th Workshop on Asian Translation

Recently, numbers of works shows that the performance of neural machine translation (NMT) can be improved to a certain extent with using visual information. However, most of these conclusions are drawn from the analysis of experimental results based on a limited set of bilingual sentence-image pairs, such as Multi30K.In these kinds of datasets, the content of one bilingual parallel sentence pair must be well represented by a manually annotated image,which is different with the actual translation situation. we propose an open-vocabulary image retrieval methods to collect descriptive images for bilingual parallel corpus using image search engine, and we propose text-aware attentive visual encoder to filter incorrectly collected noise images. Experiment results on Multi30K and other two translation datasets show that our proposed method achieves significant improvements over strong baselines.