Mitigating Translationese in Low-resource Languages: The Storyboard Approach

TL;DR

This paper introduces a storyboard-based data collection method to reduce translationese effects in low-resource languages, aiming to improve fluency over traditional translation-based approaches.

Contribution

It presents a novel visual stimulus approach for eliciting natural language data, addressing limitations of translation-based data collection in low-resource language settings.

Findings

Storyboard method yields more fluent sentences.

Traditional translation achieves higher accuracy.

Trade-off between fluency and accuracy observed.

Abstract

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.