Skip-Thought Vectors


We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoderdecoder 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. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice. We will make our encoder publicly available.

Extracted Key Phrases

9 Figures and Tables

Citations per Year

362 Citations

Semantic Scholar estimates that this publication has 362 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Kiros2015SkipThoughtV, title={Skip-Thought Vectors}, author={Ryan Kiros and Yukun Zhu and Ruslan Salakhutdinov and Richard S. Zemel and Raquel Urtasun and Antonio Torralba and Sanja Fidler}, booktitle={NIPS}, year={2015} }