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Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
2019
Proceedings of the 2019 Conference of the North
We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the contextdependent spaces. This
doi:10.18653/v1/n19-1162
dblp:conf/naacl/SchusterRBG19
fatcat:ns2bxzatkjdovnyqzxegtw53i4