skipthoughts

Evaluation of skip-thoughts model outlined in Kiros paper (https://arxiv.org/pdf/1506.06726.pdf)

An encoder decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. The skip-gram model is abstracted to the sentence level. Instead of a word predicting surrounding words within a setnence, this model aims to encode a sentence to predict surrounding sentences!

Dependencies

This code is written in python. To use it you will need:

• Python 2.7
• Theano 0.7
• A recent version of NumPy and SciPy
• scikit-learn
• NLTK 3
• Keras (for Semantic-Relatedness experiments only)
• gensim (for vocabulary expansion when training new models)