DeepQA is a library built on top of Keras to make NLP easier. There are four main benefits to this library:
- It is hard to get NLP right in Keras. There are a lot of issues around padding sequences and masking that are not handled well in the main Keras code, and we have well-tested code that does the right thing for, e.g., computing attentions over padded sequences, or distributing text encoders across several sentences or words.
- We have implemented a base class,
TextTrainer, that provides a nice, consistent API around building NLP models in Keras. This API has functionality around processing data instances, embedding words and/or characters, easily getting various kinds of sentence encoders, and so on.
- We provide a nice interface to training, validating, and debugging Keras models. It is very
easy to experiment with variants of a model family, just by changing some parameters in a JSON
file. For example, you can go from using fixed GloVe vectors to represent words, to fine-tuning
those embeddings, to using a concatenation of word vectors and a character-level CNN to
represent words, just by changing parameters in a JSON experiment file. If your model is built
TextTrainerAPI, all of this works transparently to the model class - the model just knows that it’s getting some kind of word vector.
- We have implemented a number of state-of-the-art models, particularly focused around question answering systems (though we’ve dabbled in models for other tasks, as well). The actual model code for these systems are typically 50 lines or less.
This library has several main components:
trainingmodule, which has a bunch of helper code for training Keras models of various kinds.
modelsmodule, containing implementations of actual Keras models grouped around various prediction tasks.
layersmodule, which contains code for custom Keras Layers that we have written.
datamodule, containing code for reading in data from files and converting it into numpy arrays suitable for use with Keras.
commonmodule, which has a few random things dealing with reading parameters and a few other things.