  The long-distance agreement, evidence for syntactic structure, is
increasingly used to assess the syntactic generalization of Neural Language
Models. Much work has shown that transformers are capable of high accuracy in
varied agreement tasks, but the mechanisms by which the models accomplish this
behavior are still not well understood. To better understand transformers'
internal working, this work contrasts how they handle two superficially similar
but theoretically distinct agreement phenomena: subject-verb and object-past
participle agreement in French. Using probing and counterfactual analysis
methods, our experiments show that i) the agreement task suffers from several
confounders which partially question the conclusions drawn so far and ii)
transformers handle subject-verb and object-past participle agreements in a way
that is consistent with their modeling in theoretical linguistics.
