Summary of Navigating the Minefield Of Mt Beam Search in Cascaded Streaming Speech Translation, by Rastislav Rabatin et al.
Navigating the Minefield of MT Beam Search in Cascaded Streaming Speech Translation
by Rastislav Rabatin, Frank Seide, Ernie Chang
First submitted to arxiv on: 26 Jun 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 adapt the beam-search algorithm for machine translation to operate in real-time speech translation systems. This adaptation proved to be complex due to four key challenges: processing incomplete words from automatic speech recognition (ASR), emitting translations with minimal latency, handling hypotheses of unequal length and different model state, and handling sentence boundaries. The authors present a beam-search realization that addresses these challenges, providing an increase in BLEU score by 1 point compared to greedy search, while reducing CPU time by up to 40% and character flicker rate by 20+% compared to a baseline heuristic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes the well-known beam-search algorithm for machine translation and makes it work for real-time speech translation. This wasn’t easy because there were four big challenges: ASR gives us incomplete words, we need to translate fast enough so users don’t notice any delay, our hypotheses might be different lengths or have different “states”, and we need to know when sentences start and end. The researchers found a way to make it work, which makes the translations better (BLEU score up 1 point) and faster (CPU time down by 40% and character flicker rate down by 20%). |
Keywords
» Artificial intelligence » Bleu » Translation
