Segmentation-Free Streaming Machine Translation
Javier Iranzo-Sánchez Jorge Iranzo-Sánchez Adrià Giménez Jorge Civera Alfons Juan
Streaming Machine Translation (MT) is the task of translating an unbounded
input text stream in real-time. The traditional cascade approach, which
combines an Automatic Speech Recognition (ASR) and an MT system, relies on an
intermediate segmentation step which splits the transcription stream into
sentence-like units. However, the incorporation of a hard segmentation
constrains the MT system and is a source of errors. This paper proposes a
Segmentation-Free framework that enables the model to translate an unsegmented
source stream by delaying the segmentation decision until the translation has
been generated. Extensive experiments show how the proposed Segmentation-Free
framework has better quality-latency trade-off than competing approaches that
use an independent segmentation model. Software, data and models will be
released upon paper acceptance.