ESCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing

eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing Matteo Negri(1), Marco Turchi(1), Rajen Chatterjee(1,2), Nicola Bertoldi(1) (1)Fondazione Bruno Kessler, (2)University of Trento Trento, Italy {negri, turchi, chatterjee, bertoldi}@fbk.eu Abstract Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sentence, examples of translation errors with the corresponding corrections made by a human post-editor. Ideally, a large amount of data of this kind should allow the model to learn reliable correction patterns and effectively apply them at test stage on unseen (source, MT) pairs. In practice, however, their limited availability calls for solutions that also integrate in the training process other sources of knowledge. Along this direction, state-of-the-art results have been recently achieved by systems that, in addition to a limited amount of available training data, exploit artificial corpora that approximate elements of the “gold” training instances with automatic translations. Following this idea, we present eSCAPE, the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. eSCAPE consists of millions of entries in which the MT element of the training triplets has been obtained by translating the source side of publicly-available parallel corpora, and using the target side as an artificial human post-edit. Translations are obtained both with phrase-based and neural models. For each MT paradigm, eSCAPE contains 7.2 million triplets for English–German and 3.3 millions for English–Italian, resulting in a total of 14,4 and 6,6 million instances respectively. The usefulness of eSCAPE is proved through experiments in a general-domain scenario, the most challenging one for automatic post-editing. For both language directions, the models trained on our artificial data always improve MT quality with statistically significant gains. The current version of eSCAPE can be freely downloaded from: http://hltshare.fbk.eu/QT21/eSCAPE.html. Keywords: Automatic Post-editing, Machine Translation 1. Introduction Automatic post-editing (APE) for machine translation (MT) aims to fix recurrent errors made by the MT decoder by learning from correction examples. As a post-processing step, APE has several possible applications, especially in black-box scenarios (e.g. when working with a third-party translation engine) in which the MT system is used “as is” and is not directly accessible for retraining or for more rad- ical internal modifications. In such scenarios, as pointed out by Chatterjee et al. (2015), APE systems can help to: i) improve MT output by exploiting information unavail- able to the decoder, or by performing a deeper text analysis that is too expensive at the decoding stage; ii) provide pro- fessional translators with improved MT output quality to reduce (human) post-editing effort, and iii) adapt the out- put of a general-purpose MT system to the lexicon/style re- quested in a specific application domain. The training of APE systems usually relies on data sets comprising (source, MT, human post-edit) triplets, in which the source sentence in a given language has been automatically translated to produce the MT element that, in turn, has been manually corrected to produce the hu- man post-edit. In this supervised learning setting, the goal is …

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