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        <title>Multimodal Neural Machine Translation with Search Engine Based Image Retrieval</title>
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        <namePart type="given">ZhenHao</namePart>
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        <namePart type="given">XiaoBing</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Multimodal Neural Machine Translation with Search Engine Based Image Retrieval
%A Tang, ZhenHao
%A Zhang, XiaoBing
%A Long, Zi
%A Fu, XiangHua
%S Proceedings of the 9th Workshop on Asian Translation
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Gyeongju, Republic of Korea
%F tang-etal-2022-multimodal
%X 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.
%U https://aclanthology.org/2022.wat-1.11
%P 89-98

Markdown (Informal)

[Multimodal Neural Machine Translation with Search Engine Based Image Retrieval](https://aclanthology.org/2022.wat-1.11) (Tang et al., WAT 2022)

• Multimodal Neural Machine Translation with Search Engine Based Image Retrieval (Tang et al., WAT 2022)

ACL

• ZhenHao Tang, XiaoBing Zhang, Zi Long, and XiangHua Fu. 2022. Multimodal Neural Machine Translation with Search Engine Based Image Retrieval. In Proceedings of the 9th Workshop on Asian Translation, pages 89–98, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.