  In this paper we exploit cross-lingual models to enable dialogue act
recognition for specific tasks with a small number of annotations. We design a
transfer learning approach for dialogue act recognition and validate it on two
different target languages and domains. We compute dialogue turn embeddings
with both a CNN and multi-head self-attention model and show that the best
results are obtained by combining all sources of transferred information. We
further demonstrate that the proposed methods significantly outperform related
cross-lingual DA recognition approaches.
