[Solved] ECE467-Natural Language Processing Programming Project #3

Default project: Use one of the libraries to re-implement Project #1, for any one of the three datasets. If you choose dataset #2 or #3 (the ones for which I did not provide my test set), you will need to split the dataset into a smaller training set, probably a tuning set, and a test set. Use the training set along with the tuning set to experiment with architectures and hyperparameters, and only test the final version on the test set. If you choose dataset #1, keep the test set as is, and youll probably want to split the training set into a training set and a tuning set. My expectation is that a deep learning technique (including any recurrent neural network variation) will likely work best for the second dataset, because the documents contain only single sentences. I am not sure if the results will be good for any of these datasets, because they are very small by deep learning standards. I have not tried this myself yet, and I would not be surprised either way.

Regardless if you choose the default project or something entirely different, I expect more from a two- or a three-person group than from an individual. From an individual, just getting a project to work with a single reasonable architecture is enough. From a group, Id like to see more experimentation (with architectures, hyperparameters, etc.)

When you are complete, you should submit a SHORT writeup (one to three pages) explaining:

• Which library (TensorFlow or PyTorch) did you use?
• What task did you work on?
• What dataset did you use?
• How was the datasets split into training, tuning, and testing portions?
• What architectures and other variables did you experiment with? (Include types of networks, numbers and sizes of layers of layers, hyperparameters, etc.)
• Summarize the result of the things that were varied.
• What were the choices for the final network?
• What were the final results?