Summary of Isometric Neural Machine Translation Using Phoneme Count Ratio Reward-based Reinforcement Learning, by Shivam Ratnakant Mhaskar et al.
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning
by Shivam Ratnakant Mhaskar, Nirmesh J. Shah, Mohammadi Zaki, Ashishkumar P. Gudmalwar, Pankaj Wasnik, Rajiv Ratn Shah
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers developed an innovative Automatic Video Dubbing (AVD) pipeline that utilizes Reinforcement Learning (RL) for neural machine translation. The traditional AVD pipeline typically involves ASR, NMT, and TTS modules, but the authors focused on aligning phonemes instead of characters or words to ensure synchronization between video and audio. They presented an isometric NMT system using RL, optimizing phoneme count alignment in source-target language sentence pairs. To evaluate their models, they proposed a Phoneme Count Compliance (PCC) score, which showed a 36% improvement over state-of-the-art models on English-Hindi language pairs. Furthermore, the authors introduced a student-teacher architecture within their RL approach to balance phoneme count and translation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making videos sound better by using a special kind of machine learning called Reinforcement Learning. Normally, when we make videos with subtitles, we match the words in the original language with the translated words. But this paper does something different – it matches the sounds in the two languages instead. This helps the subtitles fit better with the audio and makes the video look more natural. The researchers created a new way to do this using Reinforcement Learning, which worked really well on English-Hindi videos. They even came up with a special score to measure how well their method did. |
Keywords
* Artificial intelligence * Alignment * Machine learning * Reinforcement learning * Translation
