Simultaneous machine translation (SiMT) aims to deliver real-time translations as a source language, spoken or written. Traditionally, this requires models that control when to “read” more of the source and when to “write” the translation — decisions that rely on intensive model training, complex model designs, and significant computing power.

Now, researchers Libo Zhao, Jing Li, and Ziqian Zeng from Hong Kong Polytechnic University and South China University of Technology have introduced PsFuture, a zero-shot, adaptable read/write policy that enables SiMT models to make real-time translation decisions without additional training.

The researchers said they drew inspiration from human interpreters, who dynamically decide when to listen and when to speak based on evolving contexts. “Interpreters shift from listening to translating upon anticipating that further future words would not impact their current decisions,” they explained.

PsFuture allows translation models to make similar, context-aware decisions, leveraging “the model’s inherent linguistic comprehension and translation proficiency” and eliminating the need for further training.

Simulated Look-Ahead

Rather than relying on a fixed number of source words to determine the right time to start translating, PsFuture allows a model to anticipate what’s coming next. By using pseudo-future information — a simulated, brief “look-ahead” similar to how interpreters anticipate what might come next in a sentence — the model assesses if additional context would change its next translation output. If not, the model proceeds with translating. If more context is needed, it waits to “read” further. 

Source: Slator

Full article: https://slator.com/new-real-time-ai-translation-method-draws-inspiration-simultaneous-interpreters/