Neural Machine Translation For Low Resource Languages
This work addresses the problem of improving translation quality for low-resource languages, which is an incremental advancement in the field.
The paper tackles the challenge of neural machine translation for low-resource languages by building a model based on mBART, augmented with techniques like back translation and transfer learning, aiming to achieve state-of-the-art results.
Neural Machine translation is a challenging task due to the inherent complex nature and the fluidity that natural languages bring. Nonetheless, in recent years, it has achieved state-of-the-art performance in several language pairs. Although, a lot of traction can be seen in the areas of multilingual neural machine translation (MNMT) in the recent years, there are no comprehensive survey done to identify what approaches work well. The goal of this paper is to investigate the realm of low resource languages and build a Neural Machine Translation model to achieve state-of-the-art results. The paper looks to build upon the mBART language model and explore strategies to augment it with various NLP and Deep Learning techniques like back translation and transfer learning. This implementation tries to unpack the architecture of the NMT application and determine the different components which offers us opportunities to amend the said application within the purview of the low resource languages problem space.