The more useful part about neural net in NLP is that you can train complexe end to end models that end up performing better at their task than anything else com

The more useful part about neural net in NLP is that you can train complexe end to end models that end up performing better at their task than anything else composite. An example of this is neural translation with the Attention is all you need paper (https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762), which is the top of the line in translation. An other example of this is Microsoft's R-NET (https://www.microsoft.com/en-us/research/publication/mrc/ https://www.microsoft.com/en-us/research/publication/mrc/), which are the top of the line at question answering (https://rajpurkar.github.io/SQuAD-explorer/ https://rajpurkar.github.io/SQuAD-explorer/) (https://research.fb.com/downloads/babi/ https://research.fb.com/downloads/babi/) An other cool example of complex end-to-end network are Stanford's Dynamic Coattention Networks (https://arxiv.org/abs/1611.01604 https://arxiv.org/abs/1611.01604)