2 research outputs found
Online Multi-User Adaptive Statistical Machine Translation
In this paper we investigate the problem of adapting a machine translation system to the
feedback provided by multiple post-editors. It is well know that translators might have very
different post-editing styles and that this variability hinders the application of online
learning methods, which indeed assume a homogeneous source of adaptation data.
We hence propose multi-task learning to leverage bias information from each single post-editors in order to constrain the
evolution of the SMT system. A new framework for significance testing with sentence level metrics is described which shows that
Multi-Task learning approaches outperforms existing online learning approaches, with
significant gains of 1.24 and 1.88 TER score over
a strong online adaptive baseline, on a test set of post-edits produced by four translators texts
and on a popular benchmark with multiple references, respectively