REINA: Regularized Entropy Information-Based Loss for Efficient Simultaneous Speech Translation
Nameer Hirschkind, Joseph Liu, Mahesh Kumar Nandwana, Xiao Yu
2025-08-08
Summary
This paper talks about REINA, a new approach that helps translation models decide when to wait for more speech input so they can translate better while still being fast.
What's the problem?
The problem is that in simultaneous speech translation, models have to balance between starting the translation quickly and making sure the translation is accurate, which is difficult because waiting too long slows things down but starting too early can cause mistakes.
What's the solution?
The solution was to create a new loss function called REINA that guides the model to wait just the right amount of time based on how much useful information is gained, helping it find a better balance between speed and quality.
Why it matters?
This matters because better simultaneous translation makes real-time conversations across languages smoother and clearer, which is helpful for things like international meetings, live broadcasts, and travel.
Abstract
A novel loss function, REINA, optimizes the latency-quality tradeoff in Simultaneous Speech Translation by adaptively waiting for more input based on information gain.