This paper describes the latest developments in the PeEn-SMT system, specifically covering experiments with Grafix, an APE component developed for PeEn-SMT. The success of well-designed SMT systems has made this approach one of the most popular MT approaches. However, MT output is often seriously grammatically incorrect. This is more prevalent in SMT since this approach is not language-specific. This system works with Persian, a morphologically rich language, so post-editing output is an important step in maintaining translation fluency. Grafix performs a range of corrections on sentences, from lexical transformation to complex syntactical rearrangement. It analyzes the target sentence (the SMT output in Persian language) and attempts to correct it by applying a number of rules which enforce consistency with Persian grammar. We show that the proposed system is able to improve the quality of the state-of-the-art English-Persian SMT systems, yielding promising results from both automatic and manual evaluation techniques.

This paper proposes a post-editing model in which our three-level rule-based automatic post-editing engine called Grafix is presented to refine the output of machine translation systems. The type of corrections on sentences varies from lexical transformation to complex syntactical rearrangement. The experimental results both in manual and automatic evaluations show that the proposed system is able to improve the quality of our state-of-the-art English-Persian SMT system. 1

Abstract: We present a method for improving machine translation (MT) evaluation by targeted paraphrasing of reference sentences. For this purpose, we employ MT sys-tems themselves and adapt them for translating within a single language. We describe this attempt on two types of MT systems – phrase-based and rule-based. Initially, we experiment with the freely available SMT system Moses. We create translation models from two available sources of Czech paraphrases – Czech WordNet and the Meteor Para-phrase tables. We extended Moses by a new feature that makes the translation targeted. However, the results of this method are inconclusive. In the view of errors appearing in the new paraphrased sentences, we propose another so-lution – targeted paraphrasing using parts of a rule-based translation system included in the NLP framework Treex. 1

Using cross-lingual question answering (CLQA), users can find information in languages that they do not know. In this thesis, we consider the broader problem of CLQA with result translation, where answers retrieved by a CLQA system must be translated back to the user’s language by a machine translation (MT) system. This task is challenging because answers must be both relevant to the question and adequately translated in order to be correct. In this work, we show that integrating the MT closely with cross-lingual retrieval can improve result relevance and we further demonstrate that automatically correcting errors in the MT output can improve the adequacy of translated results. To understand the task better, we undertake detailed error analyses examining the impact of MT errors on CLQA with result translation. We identify which MT errors are most detrimental to the task and how different cross-lingual information retrieval (CLIR) systems respond to different kinds of MT errors. We describe two main types of CLQA errors caused by MT errors: lost in retrieval errors, where relevant results are not returned, and lost in translation errors, where relevant results are perceived irrelevant due to inadequate MT. To address the lost in retrieval errors, we introduce two novel models for cross-lingual informa-