  We target the task of cross-lingual Machine Reading Comprehension (MRC) in
the direct zero-shot setting, by incorporating syntactic features from
Universal Dependencies (UD), and the key features we use are the syntactic
relations within each sentence. While previous work has demonstrated effective
syntax-guided MRC models, we propose to adopt the inter-sentence syntactic
relations, in addition to the rudimentary intra-sentence relations, to further
utilize the syntactic dependencies in the multi-sentence input of the MRC task.
In our approach, we build the Inter-Sentence Dependency Graph (ISDG) connecting
dependency trees to form global syntactic relations across sentences. We then
propose the ISDG encoder that encodes the global dependency graph, addressing
the inter-sentence relations via both one-hop and multi-hop dependency paths
explicitly. Experiments on three multilingual MRC datasets (XQuAD, MLQA,
TyDiQA-GoldP) show that our encoder that is only trained on English is able to
improve the zero-shot performance on all 14 test sets covering 8 languages,
with up to 3.8 F1 / 5.2 EM improvement on-average, and 5.2 F1 / 11.2 EM on
certain languages. Further analysis shows the improvement can be attributed to
the attention on the cross-linguistically consistent syntactic path.
