This paper builds TopicVD, a topic-based documentary video-subtitle translation dataset, and shows with a cross-modal attention model that visual and contextual information improve BLEU scores.
Multimodal Pivots for Image Caption Translation
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abstract
We present an approach to improve statistical machine translation of image descriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval over a database of images that are captioned in the target language, and use the captions of the most similar images for crosslingual reranking of translation outputs. Our approach does not depend on the availability of large amounts of in-domain parallel data, but only relies on available large datasets of monolingually captioned images, and on state-of-the-art convolutional neural networks to compute image similarities. Our experimental evaluation shows improvements of 1 BLEU point over strong baselines.
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TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
This paper builds TopicVD, a topic-based documentary video-subtitle translation dataset, and shows with a cross-modal attention model that visual and contextual information improve BLEU scores.