  This paper addresses the issue of generalization for Semantic Parsing in an
adversarial framework. Building models that are more robust to inter-document
variability is crucial for the integration of Semantic Parsing technologies in
real applications. The underlying question throughout this study is whether
adversarial learning can be used to train models on a higher level of
abstraction in order to increase their robustness to lexical and stylistic
variations.We propose to perform Semantic Parsing with a domain classification
adversarial task without explicit knowledge of the domain. The strategy is
first evaluated on a French corpus of encyclopedic documents, annotated with
FrameNet, in an information retrieval perspective, then on PropBank Semantic
Role Labeling task on the CoNLL-2005 benchmark. We show that adversarial
learning increases all models generalization capabilities both on in and
out-of-domain data.
