“…Cross-language transfer learning employing deep neural networks has widely been studied in the areas of natural language processing (Ma and Xia, 2014;Guo et al, 2015;Kim et al, 2017;Kann et al, 2017;Cotterell and Duh, 2017), speech recognition (Xu et al, 2014;Huang et al, 2013), and information retrieval (Vulić and Moens, 2015;Sasaki et al, 2018;Litschko et al, 2018). Learning the language structure (e.g., morphology, syntax) and transferring knowledge from the source language to the target language is the main underneath challenge, and has been thoroughly investigated for a wide variety of NLP applications, including sequence tagging (Yang et al, 2016;Buys and Botha, 2016), name entity recognition (Xie et al, 2018), dependency parsing (Tiedemann, 2015;Agić et al, 2014), entity coreference resolution and linking , sentiment classification (Zhou et al, 2015(Zhou et al, , 2016b, and question answering (Joty et al, 2017).…”