  This paper presents a new semantic frame parsing model, based on Berkeley
FrameNet, adapted to process spoken documents in order to perform information
extraction from broadcast contents. Building upon previous work that had shown
the effectiveness of adversarial learning for domain generalization in the
context of semantic parsing of encyclopedic written documents, we propose to
extend this approach to elocutionary style generalization. The underlying
question throughout this study is whether adversarial learning can be used to
combine data from different sources and train models on a higher level of
abstraction in order to increase their robustness to lexical and stylistic
variations as well as automatic speech recognition errors. The proposed
strategy is evaluated on a French corpus of encyclopedic written documents and
a smaller corpus of radio podcast transcriptions, both annotated with a
FrameNet paradigm. We show that adversarial learning increases all models
generalization capabilities both on manual and automatic speech transcription
as well as on encyclopedic data.
