Hi there,

I’ve been working on a project about SOTA methods in learning sentence embeddings. In particular I took a look at Quickthoughts, which uses a word2vec-like objective, seeking to identify “related” sentences.

As such I’ve produced an open PyTorch implementation of QuickThoughts here: https://github.com/jcaip/quickthoughts

In adddition I tried to use the theoretical framework described by Arora et al. about contrastive unsupervised representation learning, to examine the effect of changing the context_size of Quickthoughts, and offer a possible modification that should increase performance. Unfortunately this was unsuccessful, but hopefully you’ll still find the work interesting.

Post: https://jcaip.github.io/Quickthoughts/

Please lmk if you have any questions/comments, thanks for reading!