SPINE: SParse Interpretable Neural Embeddings

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Authors Danish Pruthi, Eduard Hovy, Harsh Jhamtani, Anant Subramanian, Taylor Berg-Kirkpatrick
Journal/Conference Name 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Paper Category
Paper Abstract 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. Through large scale human evaluation, we report that our resulting word embedddings are much more interpretable than the original GloVe and word2vec embeddings. Moreover, our embeddings outperform existing popular word embeddings on a diverse suite of benchmark downstream tasks.
Date of publication 2017
Code Programming Language Python
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