Thread by @nlopitz

Sentence embeddings (e.g., SBERT) are powerful -- but we just don't know what is crammed into a %&!$# vector 😵‍💫.

💥So in our new paper, we use Abstract Meaning Representation (AMR) to make sentence embeddings more explainable! #AACL2022 #nlproc #MachineLearning (1/3)

Interesting: Yes, we use AMR -- but we don't need an AMR parser🤯. Therefore, we don't lose efficiency 🚀. The accuracy 🎯 is also preserved, and sometimes even improved (for argument similarity, we achieve a new state-of-the-art). (2/3)

(3/3)
Paper: arxiv.org/abs/2206.07023
Code: github.com/flipz357/S3BERT

(I'd like to quickly grab the opportunity here and mention @Nils_Reimers and @mdtux, since they definitely inspired me a bit with their cool works on sentence embeddings and AMR.)