In a December 29, 2024 paper, researchers Pratik Rakesh Singh, Mohammadi Zaki, and Pankaj Wasnik from Sony Research India introduced a framework aimed at improving translations for entertainment content in Indian languages.

They claim this is “the first of its kind,” using a blend of context awareness and style adaptation to produce translations that are “not only accurate but also engaging for the target audience.”

The researchers explained that traditional machine translation (MT) systems often struggle with entertainment content because they typically translate sentences in isolation. This can lead to translations that feel “disconnected” and fail to capture the emotional depth and cultural references of the original dialogue. This limitation is particularly pronounced in entertainment, where interconnected conversations and subtle narrative cues are essential.

In entertainment translation, “the challenge lies in preserving the context, mood, and style of the original content while also incorporating creativity and considering regional dialects, idioms, and other linguistic nuances,” the researchers said. 

To tackle these challenges, they developed the Context and Style Aware Translation (CASAT) framework, which integrates both context and style into the translation process.

The CASAT framework begins by segmenting the input text — such as dialogues from movies or series — into smaller sections called “sessions.” Each session groups dialogues that share a consistent genre or mood, such as comedy or drama. This segmentation enables CASAT to focus on the specific emotional and narrative elements of each session.

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For each session, CASAT estimates two critical components: context and style. Context refers to the narrative framework surrounding the dialogue, while style captures the emotional tone and cultural nuances, such as humor, seriousness, or excitement. By understanding these elements, the framework can create translations that resonate more deeply with the target audience.

To facilitate this process, CASAT employs a context retrieval module that extracts relevant scenes or dialogues from a vector database, ensuring that the translation is grounded in the appropriate narrative framework. Additionally, a domain adaptation module analyzes dialogues at both the session and sentence levels to derive insights about the intended emotional tone and intent.

Once the context and style are estimated, CASAT generates a tailored prompt that combines these elements. The tailored prompt is then fed into a large language model (LLM), which generates translations that are not only accurate but also reflect the intended emotional tone and cultural nuances of the original content.

Superior Performance

The researchers evaluated CASAT’s effectiveness using metrics such as COMET scores and win ratios. CASAT consistently outperformed baseline LLMs and traditional MT systems like IndicTrans2 and NLLB, delivering higher-quality translations that were more engaging and contextually relevant. 

“Our method demonstrates superior performance by consistently incorporating plot and style information compared to directly prompting creativity in LLMs,” the researchers said.

They also found that context alone significantly enhances translation quality, whereas incorporating style alone has minimal impact. Combining both context and style achieved the greatest improvements.

The researchers emphasized that CASAT is designed to be language and model-agnostic. “Our method is both language and LLM-agnostic, making it a general-purpose tool,” they concluded.