Transformers for Language Translation, Classification and Segmentation challenge

Introduction

The challenge of translating text from one language to another is one of the cornerstones of natural language processing (NLP). Its major goal is to generate target-language text that faithfully conveys the intended meaning of the input material. The objective of this task is to take an English statement as input and output a French translation of it, or any other language of preference. Because of the importance of language translation to the area of NLP, the original transformer model was created with that objective in mind.

The models in the Table below that use transformers and have performed well on the Language Translation task are listed there. In order to improve the flow of information and knowledge between people of diverse linguistic backgrounds, these models play a crucial role. When considering the breadth of NLP’s potential impact in fields as varied as business, science, education, and social interactions, it’s clear that the language translation task is an essential topic of study. The table presents many transformer-based models that show promise for enhancing the state-of-the-art in this area, opening the door to novel methods to language translation (Chowdhary & Chowdhary, 2020; Monroe; 2017; Hirschberg; & Manning; 2015).

👁 image