The computational complexity of training this seq2seq model is higher than training the word embedding neural language model, so in practice this is likely unfeasible.Upvote PredictorThere are a number of directions to explore when improving the upvote predictor model..At a glance, the hyperparameters can almost certainly be improved for all of the models we tried..Feeding the models into an XGBoost-based supervisor models would almost certainly lead to an improvement, if we could somehow overcome the GPU memory issues we faced when training that model..Exploring different neural network architectures could also be useful, especially since the embeddings might need to be treated differently from independent features..There were also a number of features that we had to forego due to lack of computing power, time, or both, and it could be very helpful to include those in the next iteration of the model..Regardless of what steps we take, we will undoubtedly need access to greater computational resources.Combining ModelsThough both models can separately help improve the user experience for Redditors, a combined model could do even more for a user..The upvote predictor could be used to measure the projected quality of the comment and this information could be fed into the keyboard for completions that help increase comment quality..This would mean a better experience for contributors as well as consumers.. More details