Knowledge Graphs @ EMNLP 2021

#artificialintelligence 

If you are an experienced reader of such digests (or previous posts) then you know pretty well the abundance of KG-augmented LMs published at every conference and uploaded to arxiv weekly. If you feel lost -- I can assure you're not the only one. This year, we finally have a sound framework and taxonomy of various KG LM approaches! The authors define 3 big families: 1 no KG supervision, probing knowledge encoded in LM params with cloze-style prompts; 2 KG supervision with entities and IDs; 3 KG supervision with relation templates and surface forms. Each family has a few branches For instance, let's have a look at 4 entity-aware models illustrated below.

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