  Zero-shot cross-lingual information extraction(IE) aims at constructing an IE
model for some low-resource target languages, given annotations exclusively in
some rich-resource languages. Recent studies based on language-universal
features have shown their effectiveness and are attracting increasing
attention. However, prior work has neither explored the potential of
establishing interactions between language-universal features and contextual
representations nor incorporated features that can effectively model
constituent span attributes and relationships between multiple spans. In this
study, a syntax-augmented hierarchical interactive encoder (SHINE) is proposed
to transfer cross-lingual IE knowledge. The proposed encoder is capable of
interactively capturing complementary information between features and
contextual information, to derive language-agnostic representations for various
IE tasks. Concretely, a multi-level interaction network is designed to
hierarchically interact the complementary information to strengthen domain
adaptability. Besides, in addition to the well-studied syntax features of
part-of-speech and dependency relation, a new syntax feature of constituency
structure is introduced to model the constituent span information which is
crucial for IE. Experiments across seven languages on three IE tasks and four
benchmarks verify the effectiveness and generalization ability of the proposed
method.
