Neural Interactive Translation Prediction

Neural Interactive Translation Prediction Rebecca Knowles rknowles@jhu.edu Philipp Koehn phi@jhu.edu Department of Computer Science, Center for Language and Speech Processing Johns Hopkins University, Baltimore, MD, 21218, USA Abstract We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6% vs. 43.3%) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means to enable practical deployment. Interactive translation prediction (also called interactive machine translation) is an editing mode for translators who interact with machine translation output. In this mode, the machine translation system makes suggestions for how to complete the translation (“auto-complete”), and the translator either accepts suggested words or writes in their own translation. When the suggestion is rejected, the machine translation system recomputes its prediction for how to complete the sentence from the given prefix and presents the corrected version to the translator. In prior work, phrase-based machine translation systems have been used for interactive translation prediction, and suggestions were made either by re-decoding constrained by the prefix (Green et al., 2014) or by searching for the prefix in the original search graph (Och et al., 2003; Barrachina et al., 2009). Recently, neural translation models have been proposed and in some cases have shown superior performance over phrase-based models (Jean et al., 2015; Sennrich et al., 2016). We propose to use such models for interactive translation prediction. Parallel to this work, Wuebker et al. (2016) also explore a similar approach to using neural MT for interactive translation prediction. The decoding mechanism for neural models provides a natural way of doing interactive translation prediction. We show that neural translation models can provide better translation prediction quality and improved recovery from rejected suggestions. We also develop efficient methods that enable neural models to meet the speed requirements of live interactive translation prediction systems. 1 Interactive Translation Prediction Interactive translation prediction leaves the translator in charge of writing the translation and places the machine translation system in an assisting role. Rather than having a translator post- edit machine translated output, the system actively makes suggestions as the translator writes their translation. This modality is similar to an auto-complete function. Whenever the translator diverges from the suggestion (by typing a word that differs from the model’s suggestion), the system recalculates (taking the translator’s input into account) and generates new suggestions. Implementations of interactive translation can be found in the CASMACAT1 (see Figure 1) and 1http://www.casmacat.eu ������������������������������������������������������ ����������������������������������������� Figure 1: Interactive translation prediction in CASMACAT: The system suggests to continue the translation with the words mehr als 18, which the user can accept by pressing the TAB key. Lilt2 computer aided translation tools. This interaction mode is preferred by translators over post-editing (Koehn, 2009). The goal of interactive translation prediction is to offer suggestions that the translator will accept. Existing approaches with statistical machine translation use the static search graph …

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