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2020 AACL AACL 2020

The ADAPT Centre’s Participation in WAT 2020 English-to-Odia Translation Task

Abstract

AbstractThis paper describes the ADAPT Centre sub-missions to WAT 2020 for the English-to-Odia translation task. We present the approaches that we followed to try to build competitive machine translation (MT) systems for English-to-Odia. Our approaches include monolingual data selection for creating synthetic data and identifying optimal sets of hyperparameters for the Transformer in a low-resource scenario. Our best MT system produces 4.96BLEU points on the evaluation test set in the English-to-Odia translation task.

🚀 Conference Pioneer — AACL 2020
🧭 Keyword Pioneer — low resource scenario
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing

Authors

Prashanth Nayak , Rejwanul Haque , Andy Way

Topics

Machine Learning > Learning Paradigms > Transfer Learning Natural Language Processing > Generation > Machine Translation Deep Learning > Models > Transformers

Keywords

hyperparameter optimization machine translation neural machine translation synthetic datum monolingual datum low resource language low resource scenario
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