Simple Automatic Post-editing for Arabic-Japanese Machine Translation

TL;DR

This paper presents a simple automatic post-editing method to improve Arabic-Japanese machine translation quality using a unique parallel corpus, demonstrating viability for less supported language pairs in specific domains.

Contribution

It introduces a straightforward post-editing technique to adapt neural MT systems for Arabic-Japanese translation using a specialized parallel corpus.

Findings

Post-editing improved translation quality significantly.

Method is effective for low-resource, domain-specific language pairs.

Approach is simple and adaptable to other low-resource scenarios.

Abstract

A common bottleneck for developing machine translation (MT) systems for some language pairs is the lack of direct parallel translation data sets, in general and in certain domains. Alternative solutions such as zero-shot models or pivoting techniques are successful in getting a strong baseline, but are often below the more supported language-pair systems. In this paper, we focus on Arabic-Japanese machine translation, a less studied language pair; and we work with a unique parallel corpus of Arabic news articles that were manually translated to Japanese. We use this parallel corpus to adapt a state-of-the-art domain/genre agnostic neural MT system via a simple automatic post-editing technique. Our results and detailed analysis suggest that this approach is quite viable for less supported language pairs in specific domains.