mlsanityData to 7 Oct 2026

Hermes the Polyglot: A Unified Framework to Enhance Expressiveness for Multimodal Interlingual Subtitling

arXiv 2602.00597 ↗First posted 31 Jan 2026cs.CL cs.AIcited by 2

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Abstract (arXiv)

Interlingual subtitling, which translates subtitles of visual media into a target language, is essential for entertainment localization but has not yet been explored in machine translation. Although Large Language Models (LLMs) have significantly advanced the general capabilities of machine translation, the distinctive characteristics of subtitle texts pose persistent challenges in interlingual subtitling, particularly regarding semantic coherence, pronoun and terminology translation, and translation expressiveness. To address these issues, we present Hermes, an LLM-based automated subtitling framework. Hermes integrates three modules: Speaker Diarization, Terminology Identification, and Expressiveness Enhancement, which effectively tackle the above challenges. Experiments demonstrate that Hermes achieves state-of-the-art diarization performance and generates expressive, contextually coherent translations, thereby advancing research in interlingual subtitling.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 61 references that are in the map. First posted on arXiv 31 Jan 2026.

Builds on

31 of 61 references.

References · 31

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19 more
  1. 2023Generative Judge for Evaluating Alignmentcited by 163
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026CineSRD: Leveraging Visual, Acoustic, and Linguistic Cues for Open-World Visual Media Speaker Diarizationcited by 0
  2. 2026Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translationcited by 0