From Simultaneous to Streaming Machine Translation by Leveraging Streaming History

TL;DR

This paper extends simultaneous machine translation to streaming scenarios by leveraging streaming history, resulting in significant quality improvements demonstrated on IWSLT tasks.

Contribution

It introduces a novel approach that incorporates streaming history into the translation process, enhancing quality over existing systems.

Findings

Significant quality gains on IWSLT tasks

Outperforms existing state-of-the-art systems

Effective use of streaming history in translation

Abstract

Simultaneous Machine Translation is the task of incrementally translating an input sentence before it is fully available. Currently, simultaneous translation is carried out by translating each sentence independently of the previously translated text. More generally, Streaming MT can be understood as an extension of Simultaneous MT to the incremental translation of a continuous input text stream. In this work, a state-of-the-art simultaneous sentence-level MT system is extended to the streaming setup by leveraging the streaming history. Extensive empirical results are reported on IWSLT Translation Tasks, showing that leveraging the streaming history leads to significant quality gains. In particular, the proposed system proves to compare favorably to the best performing systems.