In the previous sections, we discussed Doc2Vec and fastText extensively. We will build on the concepts we learned about there and try to understand the basic underlying concepts of another algorithm, called Sent2Vec. We will briefly touch on the Universal Sentence Encoder (USE) in the second part of this section.
Sent2Vec
Sent2Vec combines the continuous bag-of-words approach we discussed regarding Word2Vec, along with the fastText thought process of using constituent n-gram, to build sentence embeddings.
Matteo et al. devised the Sent2Vec approach, wherein contextual word embeddings and target word embeddings were learned by trying to predict the target words based on the context of the words, similar to the C-BOW approach. However, they extended the C-BOW methodology to define sentence embeddings as the average of the context word embeddings present in the sentence, wherein context word embeddings are not restricted to unigrams...
