They are clearly useful for indicating certain overarching trends, but say little about actual improvements for translation buyers or post-editors. While an increasing number of (automatic) metrics is available to assess the linguistic quality of machine translations, their interpretation remains cryptic to many users, specifically in the translation community. The Trials and Tribulations of Predicting Post-Editing Productivity The translation service will be incorporated into the Iversity MOOC platform and into the digital library portal. Finally, the evaluation output will result in more and better quality in-domain parallel data that will be fed back to the translation engine for higher quality output. entity recognition and its performance comparison between the source and the translated text, and sentiment analysis on the students’ forum posts. Unlike previous approaches to machine translation, the output quality in TraMOOC relies on a multimodal evaluation schema that involves crowdsourcing, error type markup, an error taxonomy for translation model comparison, and implicit evaluation via text mining, i.e. More specifically, videolectures, assignments, and MOOC forum text is automatically translated from English into eleven European and BRIC languages. The present work is an overview of the TraMOOC (Translation for Massive Open Online Courses) research and innovation project, a machine translation approach for online educational content. The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems.Įnhancing Access to Online Education: Quality Machine Translation of MOOC Content Experimental results on English―Polish and English―Chinese language pairs show that domain adaptation can better utilize BabelNet knowledge and performs better than other methods. By taking advantage of the knowledge in BabelNet, three different methods ― using direct training data, domain-adaptation techniques and the BabelNet API ― are proposed in this paper to obtain translations for OOVs to improve system performance. BabelNet is a multilingual encyclopedic dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named entities in a very large network of semantic relations.

This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT).

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