When we feed text to a transformer, the model has to figure out the structure of language entirely from data. Subject-verb relationships, modifier scope, negation boundaries: all of it must be learned implicitly from millions (or billions) of examples. But what if we just… told the model the structure upfront?

That’s the core idea behind Tree Matching Networks (TMN), my recent research exploring whether dependency parse trees can serve as an efficient structural prior for natural language inference. The short version: a graph neural network operating on parse trees achieved 75.20% accuracy on the SNLI benchmark versus 35.38% for a BERT baseline of comparable size, both trained from scratch on the same data and the same hardware.