"The information you'd need to choose is included in there. If you're doing this professionally, you should strive to have enough of a high-level understanding

"The information you'd need to choose is included in there. If you're doing this professionally, you should strive to have enough of a high-level understanding of NLP to be able to make these decisions without having a rubric handed to you on a silver platter. In a nutshell, though: Strive to use the simplest model that will get the job done. Less elaborate models are easier to understand and (usually) less prone to things like overfitting, so they'll be more tractable to work with in a business context. To that end: Use a convolutional net when you can get away with a small, fixed-size context window. Use an LSTM when you need long-term memory. Attention can be expensive, so you use it when you have cause to believe you can gain a lot by giving selective attention to features, and have both a lot of training data and a lot of computing resources. It's also worth considering that you might be best off going with none of these options. Cool as deep learning is, I've personally never actually been able to justify using it in a professional setting. Simpler models such as logistic regression and decision trees have characteristics that are near-useless for getting you to the top of a Kaggle leaderboard, but can be indispensable when working on many real-world business problems" - anonymous comment reply