SPINE: SParse Interpretable Neural Embeddings

Understand

Prediction without justification has limited utility.

• Much of the success of neural models can be attributed to their ability to learn rich, dense and expressive representations.
• While these representations capture the underlying complexity and latent trends in the data, they are far from being interpretable.
• We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations (word embeddings), beginning with existing word representations from state-of-the-art methods like GloVe and word2vec.