@jacobcvt12 I’m not very familiar with nlp tasks and torchtext, however I’ll try to explain the way we would like to use ignite. As this library is under active development, currently version 0.1.0, we can take a look how to adapt ignite to cover nlp problems  too. As always PR are welcome.
At first, ignite offers modularity of the training loop and flexibilty to interact at different steps during the training. In addition, various handlers (ModelCheckpoint, EarlyStopping, metrics computation) are provided out of box to simplify user code.
Concerning the trainer (an instance of Engine), in general case we need to code _update function in order to work with a particular task. Helper functions as create_supervised_trainer are handy for vanilla image classification tasks and other similar task where batch is (batch_x, batch_y), single loss function etc.
Later I’ll take a look at this example and could provide a port with ignite to better estimate usefulness of ignite vs code as without ignite 
HTH