The inclusion of cross-lingual information results in shared cross-lingual vector spaces which can: a) boost performance on monolingual tasks such as word similarity (Faruqui and Dyer, 2014;Rastogi et al., 2015;Upadhyay et al., 2016); and b) support cross-lingual tasks such as bilingual lexicon induction (Mikolov et al., 2013a;Gouws et al., 2015;Duong et al., 2016), cross-lingual information retrieval (Vulić and Moens, 2015;Mitra et al., 2016), and transfer learning for resource-lean languages Guo et al., 2015).
prior work on cross-lingual word embedding has tended not to exploit pre-existing linguistic resources such as BabelNet.