  Semantic parsing provides a way to extract the semantic structure of a text
that could be understood by machines. It is utilized in various NLP
applications that require text comprehension such as summarization and question
answering. Graph-based representation is one of the semantic representation
approaches to express the semantic structure of a text. Such representations
generate expressive and adequate graph-based target structures. In this paper,
we focus primarily on UCCA graph-based semantic representation. The paper not
only presents the existing approaches proposed for UCCA representation, but
also proposes a novel self-attentive neural parsing model for the UCCA
representation. We present the results for both single-lingual and
cross-lingual tasks using zero-shot and few-shot learning for low-resource
languages.

Computation and Language