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• W3184220414 abstract "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." @default.
• W3184220414 created "2021-08-02" @default.
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• W3184220414 date "2014-10-22" @default.
• W3184220414 modified "2023-09-26" @default.
• W3184220414 title "Online multi-user adaptive statistical machine translation." @default.
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