MultiScript30k: Leveraging Multilingual Embeddings to Extend Cross Script Parallel Data
Christopher Driggers-Ellis, Detravious Brinkley, Ray Chen, Aashish Dhawan, Daisy Zhe Wang, Christan Grant

TL;DR
MultiScript30k extends the Multi30k dataset to include diverse global languages and scripts using NLLB200 translation, enabling broader research in multilingual multimodal machine translation.
Contribution
It introduces a new multilingual extension of the Multi30k dataset covering additional languages and scripts, created via neural machine translation, to support more inclusive MMT research.
Findings
The dataset contains over 30,000 translated sentences.
Similarity metrics show high consistency with original Multi30k.
Translation quality varies across languages, with some scores comparable to previous extensions.
Abstract
Multi30k is frequently cited in the multimodal machine translation (MMT) literature, offering parallel text data for training and fine-tuning deep learning models. However, it is limited to four languages: Czech, English, French, and German. This restriction has led many researchers to focus their investigations only on these languages. As a result, MMT research on diverse languages has been stalled because the official Multi30k dataset only represents European languages in Latin scripts. Previous efforts to extend Multi30k exist, but the list of supported languages, represented language families, and scripts is still very short. To address these issues, we propose MultiScript30k, a new Multi30k dataset extension for global languages in various scripts, created by translating the English version of Multi30k (Multi30k-En) using NLLB200-3.3B. The dataset consists of over \(30000\)…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Big Data and Digital Economy
