# JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English

## TL;DR

This paper describes a hybrid neural and statistical machine translation system for Lithuanian to English news translation, achieving a BLEU score of 17.6 at WMT 2019.

## Contribution

It introduces a combined neural and statistical approach with post-editing for Lithuanian-English translation, a novel hybrid method for this language pair.

## Key findings

- Achieved BLEU score of 17.6
- Demonstrated effectiveness of hybrid translation approach
- Provided detailed system architecture and module descriptions

## Abstract

In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task. The system was created to translate news text from Lithuanian to English. To accomplish the given task, our system used a Word Embedding based Neural Machine Translation model to post edit the outputs generated by a Statistical Machine Translation model. The current paper documents the architecture of our model, descriptions of the various modules and the results produced using the same. Our system garnered a BLEU score of 17.6.