We evaluate the performance of two speaker commitment models on the CommitmentBank: a rulebased model<cite> (Stanovsky et al., 2017</cite> ) and a neuralbased one . Rule-based model<cite> Stanovsky et al. (2017)</cite> proposed a rule-based model based on a deterministic algorithm based on TruthTeller (Lotan et al., 2013) , which uses a top-down approach on a de- pendency tree and predicts speaker commitment score in [−3, 3] according to the implicative signatures (Karttunen, 2012) of the predicates, and whether the predicates are under the scope of negation and uncertainty modifiers.