  We introduce SynSE, a novel syntactically guided generative approach for
Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined
generative embedding spaces constrained within and across the involved
modalities (visual, language). The inter-modal constraints are defined between
action sequence embedding and embeddings of Parts of Speech (PoS) tagged words
in the corresponding action description. We deploy SynSE for the task of
skeleton-based action sequence recognition. Our design choices enable SynSE to
generalize compositionally, i.e., recognize sequences whose action descriptions
contain words not encountered during training. We also extend our approach to
the more challenging Generalized Zero-Shot Learning (GZSL) problem via a
confidence-based gating mechanism. We are the first to present zero-shot
skeleton action recognition results on the large-scale NTU-60 and NTU-120
skeleton action datasets with multiple splits. Our results demonstrate SynSE's
state of the art performance in both ZSL and GZSL settings compared to strong
baselines on the NTU-60 and NTU-120 datasets. The code and pretrained models
are available at https://github.com/skelemoa/synse-zsl
