Interactive translation prediction versus conventional post-editing in practice: a study with the CasMaCat  workbench

Abstract

Received: date / Accepted: date Abstract We conducted a field trial in computer-assisted professional translation to compare Interactive Translation Prediction (ITP) against conventional postediting (PE) of machine translation (MT) output. In contrast to the conventional PE set-up, where an MT system first produces a static translation hypothesis that is then edited by a professional translator (hence "post-editing"), ITP constantly updates the translation hypothesis in real time in response to user edits. Our study involved nine professional translators and four reviewers working with the webbased CasMaCat workbench. Various new interactive features aiming to assist the post-editor were also tested in this trial. Our results show that even with little training, ITP can be as productive as conventional PE in terms of the total time required to produce the final translation. Moreover, in the ITP setting translators require fewer key strokes to arrive at the final version of their translation.

A user study of neural interactive translation prediction

“…Both Macklovitch (2006) and Koehn (2009) found ITP to be an efficient alternative to unassisted translation in terms of processing time. So far, phrase-based statistical ITP has not yet proven to be faster than PE (Koehn 2009;Sanchis-Trilles et al 2014;Underwood et al 2014;Green et al 2014;Alves et al 2016;Alabau et al 2016). In this paper we present the results of an empirical study on translation productivity in ITP with an underlying neural MT system (NITP).…”

Correct-and-Memorize: Learning to Translate from Interactive Revisions

“…Interactive machine translation has been widely exploited to improve the translation by using interaction feedback from human users [Langlais et al, 2000;Simard et al, 2007;Barrachina et al, 2009;González-Rubio et al, 2013;Cheng et al, 2016] in statistic machine translation (SMT) [Yamada and Knight, 2001;Koehn et al, 2003;Chiang, 2007].…”

“…External information, such as word or char, has been verified to be effective in promoting translation [Chen et al, 2018;Zheng et al, 2018], but these methods can not employ on the interactive process directly. Our proposed revision memory is inspired by the CopyNet [Gu et al, 2016] and history cache [Tu et al, 2017], which only focus on better using source or global context in supervised learning, and is different from our model.…”

“…To obtain a high-quality translation, there are a few recent works attempt to incorporate human revision instructions into the algorithmic translation process. For example, previous interactive NMT [ Sanchis-Trilles et al, 2014;Alvaro Peris et al, 2016;Knowles and Koehn, 2016] proposes to ask human to revise the translation output from the beginning of a sentence to the end (i.e. from the left to right), and regenerates the partial translation on the right side of the * Equal contribution, part of this work was done while Rongxiang Weng was a research intern at ByteDance AI Lab.…”

Improving the Translation Environment for Professional Translators

“…Various surveys and field studies [46,[80][81][82][83][84][85] investigating human-computer interaction, show that translators value improved translation memory (TM)-machine translation (MT) integration methods (e.g., copy/paste, drag-and-drop within editor). References [86][87][88] show that reuse of sub-segments is possible through interactive translation prediction (ITP), a method in which users are presented, as they type, with translation suggestions from all available resources.…”