An Exploration of Data Augmentation Techniques for Improving English to Tigrinya Translation

2021/03/31 by Lidia Kidane, Sachin Kumar, Kidane, Lidia +3 · 3 citations
Computer Science · #Natural Language Processing Techniques #Topic Modeling #Text Readability and Simplification

paper · pdf · doi:10.48550/arxiv.2103.16789

Abstract

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, often requiring large amounts of auxiliary data to achieve competitive results. An effective method of generating auxiliary data is back-translation of target language sentences. In this work, we present a case study of Tigrinya where we investigate several back-translation methods to generate synthetic source sentences. We find that in low-resource conditions, back-translation by pivoting through a higher-resource language related to the target language proves most effective resulting in substantial improvements over baselines.

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• Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks
• Natural Language Processing for Tigrinya: Current State and Future Directions
• A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings

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