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[P] CURL: How to learn better sentence embeddings

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!

submitted by /u/kingcai
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.