Review: Skip-Thought Vectors

Given that the vectors are learnt using self-supervised skip thoughts model only a linear classifier is placed on top of the trained encoder. The proposed uni-skip, bi-skip, and combine-skip already give very good results. When skip-thoughts are combined with some basic pairwise statistics, it becomes competitive with the state-of-the-art which incorporate much more complicated features and hand-engineering. The proposed model's results indicate that skip-thought vectors are representative enough to capture image descriptions without having to learn their representations from scratch. On most tasks, skip-thoughts performs about as well as the bag-of-words baselines but fails to improve over methods whose sentence representations are learned directly for the task at hand.