Low Supervision, Low Corpus size, Low Similarity! Challenges in cross-lingual alignment of word embeddings : An exploration of the limitations of cross-lingual word embedding alignment in truly low resource scenarios

Low Supervision, Low Corpus size, Low Similarity! Challenges in cross-lingual alignment of word embeddings : An exploration of the limitations of cross-lingual word embedding alignment in truly low resource scenarios

Cross-lingual word embeddings are an increasingly important reseource in cross-lingual methods for NLP, particularly for their role in transfer learning and unsupervised machine translation, purportedly opening up the opportunity for NLP applications for low-resource languages.  However, most resear...

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Bibliographic Details
Main Author: Dyer, Andrew
Format: Others
Language:English
Published: Uppsala universitet, Institutionen för lingvistik och filologi 2019
Subjects:
word embeddings
cross-lingual
multilingual
low-resource
corpus size
Vecmap
FastText
alignment
orthogonal
eigenvalues
Laplacian
isospectral
isomorphic
bilingual lexicon induction
Language Technology (Computational Linguistics)
Språkteknologi (språkvetenskaplig databehandling)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395946

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395946

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