USAAR-SAPE: An English–Spanish Statistical Automatic Post-Editing System

Proceedings of the Tenth Workshop on Statistical Machine Translation, pages 216–221, Lisboa, Portugal, 17-18 September 2015. c⃝2015 Association for Computational Linguistics. USAAR-SAPE: An English–Spanish Statistical Automatic Post-Editing System Santanu Pal1, Mihaela Vela1, Sudip Kumar Naskar2, Josef van Genabith1 1Saarland University, Saarbr¨ucken, Germany 2Jadavpur University, Kolkata, India {santanu.pal, josef.vangenabith}@uni-saarland.de m.vela@mx.uni-saarland.de sudip.naskar@cse.jdvu.ac.in Abstract We describe the USAAR-SAPE English– Spanish Automatic Post-Editing (APE) system submitted to the APE Task orga- nized in the Workshop on Statistical Ma- chine Translation (WMT) in 2015. Our system was able to improve upon the baseline MT system output by incorporat- ing Phrase-Based Statistical MT (PBSMT) technique into the monolingual Statisti- cal APE task (SAPE). The reported final submission crucially involves hybrid word alignment. The SAPE system takes raw Spanish Machine Translation (MT) output provided by the shared task organizers and produces post-edited Spanish text. The parallel data consist of English Text, raw machine translated Spanish output, and their corresponding manually post-edited versions. The major goal of the task is to reduce the post-editing effort by improv- ing the quality of the MT output in terms of fluency and adequacy. 1 Introduction In this paper, we present the submission of Saar- land University (USAAR) to the WMT2015 APE task. The system combines a hybrid word align- ment system implementation with a monolingual PBSMT for the language pair English-Spanish (EN-ES), translating from English into Spanish. In order to achieve the desired translation qual- ity, translations provided by MT systems need to be corrected by human translators. Automatic MT post-editing (APE) (Knight and Chander, 1994) is the method of improving raw MT output, before performing human post-editing on it. The objec- tive is to decreases the amount of errors produced by the MT systems, achieving in the end a produc- tivity increase in the translation process. Usually APE tasks focus on fluency errors pro- duced by the MT system. The most frequent ones are incorrect lexical choices, incorrect word or- dering, the insertion of a word, the deletion of a word. For the WMT2015 APE task, we adapted our system in order to automatically post-edit lex- ical choice errors, word insertions and deletions. The method is also able to correct to some extent word ordering. The remainder of the paper is organized as fol- lows. Section 2 gives an overview of the related work, Section 3 describes the various components of our system, in particular the corpus preprocess- ing module, the hybrid word alignment module and the PBSMT model. In Section 4, we out- line the complete experimental setup. Section 5 presents the results of the automatic and human evaluation, followed by conclusion in Section 6. 2 Related Work In order to implement the correction of repeti- tive errors in the MT output, various automatic or semi-automatic post-processing or automatic PE techniques have been developed. Although MT output needs to be post-edited by humans to produce publishable quality translation (Roturier, 2009; TAUS/CNGL Report, 2010), it is faster and cheaper to post-edit MT output than to per- form human translation from …

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