Cross–lingual mappings of contextual word embedding ELMoMILOSHESKI, LJUPCHE (Avtor) Robnik Šikonja, Marko (Mentor) cross-lingual word embeddingscontextual word embeddingsvector word embeddingsword translationparallel corpusvector space mappingssingular value decompositionTo work with textual data, machine learning algorithms, in particular, neural networks, require word embeddings – vector representations of words in high-dimensional space. There are languages with a small amount of available resources. Exploiting the knowledge from the well-resourced languages for under-resourced languages is possible with cross-lingual embeddings by aligning the embeddings of one language with the vector space of another language. Existing methods for aligning embeddings are intended for context-independent embeddings, where every word has one representation. We propose a method, based on a dictionary and a parallel corpus aligns contextual embeddings, which capture more information about the context in which words appear. The proposed method requires a small amount of bilingual data, which is available for many language pairs. We empirically show that the proposed method outperforms the baseline obtained by alignment of context-independent embeddings.20192019-07-25 12:10:01Diplomsko delo/naloga108794VisID: 23334sl