Recommender system for post-editing of machine translation

Abstract

This paper is focused on improving the output of post-edited Machine Translation. A novel recommender system is introduced in this paper that was created to help post-editors to correct translation created by the Machine Translation. The aim of the paper is to describe the design and functionality of the proposed system. With the usage of automated parser were analysed pairs of segments from Machine Translation and corresponding post-edition. The calculation of the likelihood of the recommendation was used to get the word with the highest probability that was selected based on the similarity in words, tags and lemmas. The introduced approach can help to create a versatile recommender system that helps post-editors to improve their translation.