In this work, we propose a novel model for parsing natural language into tree structured semantic representations.
we propose a novel relaxed hybrid tree representation which jointly encodes both natural language sentences and semantics; such representations can be effectively learned with a latent-variable discriminative model where long-distance dependencies can be captured.