Coping with semantic uncertainty with VENSES

As in the previous RTE Challenge, we present a linguistically-based approach for semantic inference which is built around a neat division of labour between two main components: a grammatically-driven subsystem which is responsible for the level of predicate-arguments well-formedness and works on the output of a deep parser that produces augmented head-dependency structures. A second subsystem tries allowed logical and lexical inferences on the basis of different types of structural transformation intended to produce a semantically valid meaning corrispondence. Grammatical relations and semantic roles are used to generate a weighted score. In the current challenge, a number of additional modules have been added to cope with fine- grained inferential triggers which were not present in the previous dataset. Different levels of argumenthood have been devised in order to cope with semantic uncertainty generated by nearly-inferrable Text/Hypothesis pairs where the interpretation needs reasoning.