Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models

Understand

The competitive performance of neural machine translation (NMT) critically relies on large amounts of training data.

• However, acquiring high-quality translation pairs requires expert knowledge and is costly.
• Therefore, how to best utilize a given dataset of samples with diverse quality and characteristics becomes an important yet understudied question in NMT.
• Curriculum learning methods have been introduced to NMT to optimize a model's performance by prescribing the data input order, based on heuristics such as the assessment of noise and difficulty levels.