# The TALP-UPC System for the WMT Similar Language Task: Statistical vs Neural Machine Translation

## TL;DR

This paper compares statistical and neural machine translation approaches for similar language pairs, showing that their effectiveness varies depending on language similarity, with system performance evaluated through BLEU scores and competition results.

## Contribution

It provides a comparative analysis of statistical and neural translation methods on similar languages, highlighting the impact of language similarity on performance.

## Key findings

- Statistical approach outperforms neural for Spanish-Portuguese by 6 BLEU points.
- Neural approach surpasses statistical for Czech-Polish by 2 BLEU points.
- System achieved 1st place for Czech-Polish and 2nd for Spanish-Portuguese in WMT evaluation.

## Abstract

Although the problem of similar language translation has been an area of research interest for many years, yet it is still far from being solved. In this paper, we study the performance of two popular approaches: statistical and neural. We conclude that both methods yield similar results; however, the performance varies depending on the language pair. While the statistical approach outperforms the neural one by a difference of 6 BLEU points for the Spanish-Portuguese language pair, the proposed neural model surpasses the statistical one by a difference of 2 BLEU points for Czech-Polish. In the former case, the language similarity (based on perplexity) is much higher than in the latter case. Additionally, we report negative results for the system combination with back-translation. Our TALP-UPC system submission won 1st place for Czech-to-Polish and 2nd place for Spanish-to-Portuguese in the official evaluation of the 1st WMT Similar Language Translation task.