Quality Estimation for Synthetic Parallel Data Generation
Raphaël Rubino, Antonio Toral, Nikola Ljubešić, Gema Ramírez-Sánchez · 2014
This paper presents a novel approach for parallel data generation using machine translation and quality estimation.Our study focuses on pivot-based machine translation from English to Croatian through Slovene.We generate an English-Croatian version of the Europarl parallel corpus based on the English-Slovene Europarl corpus and the Apertium rule-based translation system for Slovene-Croatian.These experiments are to be considered as a first step towards the generation of reliable synthetic parallel data for under-resourced languages.We first collect small amounts of aligned parallel data for the Slovene-Croatian language pair in order to build a quality estimation system for sentence-level Translation Edit Rate (TER) estimation.We then infer TER scores on automatically translated Slovene to Croatian sentences and use the best translations to build an English-Croatian statistical MT system.We show significant improvement in terms of automatic metrics obtained on two test sets using our approach compared to a random selection of synthetic parallel data.