Papers with Video-guided Machine Translation

2 papers
SHIFT: Selected Helpful Informative Frame for Video-guided Machine Translation (2025.emnlp-main)

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Challenge: Video-guided machine translation (VMT) aims to improve translation quality by integrating contextual information from paired short video clips.
Approach: They propose a plug-and-play framework for video-guided machine translation with multimodal large language models.
Outcome: The proposed framework improves performance of MLLMs while reducing computational cost.
DART: Disambiguation-Aware Reasoning for Video-guided Machine Translation (2026.acl-long)

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Challenge: Video-guided Machine Translation (VMT) uses short video clips to enhance translation quality, but many samples are text-sufficient.
Approach: They propose a framework that integrates multimodal large language models’ multimodal reasoning into video-guided machine translation by using a pipeline for constructing training data based on multimodal relevance to translation.
Outcome: The proposed framework improves multimodal information utilization in video-guided machine translation, yielding gains in translation quality and computational efficiency.

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