Towards Visually-Guided Movie Subtitle Translation for Indic Languages
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Abstract (arXiv)
Movie subtitle translation is inherently multimodal, yet text-only systems often miss visual cues needed to convey emotion, action, and social nuance, especially for low-resource Indic languages (English to Hindi, Bengali, Telugu, Tamil and Kannada). We present a case study on five full-length films and compare two lightweight visual grounding strategies: structured attribute summaries from a 5-minute sliding window and free-text summaries of inter-subtitle visual gaps. Our analysis shows that temporal misalignment between subtitles and frames is a major obstacle in long-form video, often rendering indiscriminate visual grounding ineffective. However, oracle selective grounding, which replaces only the lowest-quality 20-30% of baseline segments with visual-enhanced outputs, consistently improves COMET over the text-only baseline while requiring far less visual processing. Among the two approaches, coarse attribute-based visual context summarization is more robust, capturing scene-level emotion and contextual subtle cues that text alone often misses
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References · 5
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
- 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
- 2016Multi30K: Multilingual English-German Image Descriptionscited by 250
- 2024A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learningcited by 4
- 2023Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languagescited by 2
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