Transfer learning for adversarial machine translation

Description

Neural Machine Translation (NMT) is the process of mapping a segment of words from a source language to a target language using neural networks. However, NMT systems rely on large datasets for the source and target languages, and perform poorly on low-resource languages where there is insufficient parallel data. An effective method for improving NMT on low-resource languages is to employ transfer learning, where a model trained on a high-resource language pair is used to initialize training for the low-resource language pair. In this work, we will study the effect of employing transfer learning methods on an adversarial machine translation models based on Long Short-Term Memory Recurrent Neural Networks (LSTM).

Students

• Manoj Yadav

• Frederick Norman

• Vikrame Vasudev Krishnan

• Hanyu He

• Shaochen Tan

• Amit Singh

Advisors

• Mohammad Reza Rajati

Skills Required by the team

• Python
• PyTorch
• Machine Learning
• Deep Learning