[Der_Einzige]

I've been waiting for someone to do a proper semantic search plugin in a browser for awhile. There was one awhile back called... Fuzbal ... which used word2vec and was good but has not been updated. You've implemented a more question-answer based approach. This is awesome!

I think that the real innovation will be when users are given exposure to lots of different models, and have the pros and cons of these models are properly explained to them. Maybe I want to use this on specialized bio-medical literature and would be better off with a model fine-tuned in that domain instead of on Squad.

Also, shameless self-plug, I wrote a system that does extractive summarization/highlighting of documents which is in principle very similar to what is going on here (https://github.com/Hellisotherpeople/CX_DB8). For awhile, I had a hosted, web accessible version of this system available to make it easy to show it off to interviewers. It could highlight the important parts of a web-page based on a user query at either the word, sentence, n-gram, or paragraph level. I figured that the next step was to make it a browser extension. I simply wasn't proficient enough in JS and at the time I was working on this, quantized/pruned models were slightly less good. I firmly believe that making high quality semantic search work everywhere will be an extreme (and obvious) step-forward for most peoples daily tasks. What a brave new world we are entering!

[yoavz]

Pretty cool. The embedding similarity approach makes a lot of sense. I actually this project by experimenting with computing cosine similarities of sentence embeddings [1]. But I wasn't very impressed with out-of-the-box results, and I found it difficult to set a similarity threshold for a match. QA was the second try, and the pretrained models worked better out of the box. I'm wondering if I should revisit the embedding approach now...

[1] https://github.com/UKPLab/sentence-transformers