BiTIIMT: A Bilingual Text Infilling Method for Interactive Machine Translation

Abstract

Interactive neural machine translation (INMT) is able to guarantee high-quality translations by taking human interactions into account. Existing IMT systems relying on lexical constrained decoding (LCD) enable humans to translate in a flexible translation manner beyond left-to-right. However, they typically suffer from limitations in translation efficiency and quality due to the reliance on LCD. In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation. The key idea to BiTIIMT is the Bilingual Text-infilling (BiTI) task which aims to fill missing segments in a manually revised translation for a given source sentence. We propose a simple yet effective solution by casting this task as a sequence-to-sequence task. The benefits of our solution are that it performs efficient decoding with the same complexity as the standard decoding in NMT and makes full use of revised words for better translation prediction. Experiment results show that BiTiIMT performs significantly better and faster than state-of-the-art LCD-based IMT on three translation tasks.