Unsupervised Multilingual Word Embeddings
Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and opens many new possibilities for low-resource languages. Prior art for learning UMWEs, however, merely relies on a number of independently trained Unsupervised Bilingual Word Embeddings (UBWEs) to obtain multilingual embeddings. These methods fail to leverage the interdependencies that exist among many languages. To address this shortcoming, we propose a fully unsupervised framework for learning MWEs that directly exploits the relations between all language pairs. Our model substantially outperforms previous approaches in the experiments on multilingual word translation and cross-lingual word similarity. In addition, our model even beats supervised approaches trained with cross-lingual resources.
Code (3)
Tasks
Multilingual Word EmbeddingsTranslationWord EmbeddingsWord SimilarityWord TranslationSimilar Papers 제목 키워드 기반
Multilingual Word Embeddings using Multigraphs
We present a family of neural-network--inspired models for computing continuous word representations, specifically designed to exploit both monolingual and multilingual text. This framework allows us to perform unsupervi…
Machine TranslationMultilingual Word EmbeddingsSemantic SimilaritySemantic Textual Similarity+2Unsupervised Alignment of Embeddings with Wasserstein Procrustes
A library for Multilingual Unsupervised or Supervised word Embeddings
Word EmbeddingsLearning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs
Recent research has discovered that a shared bilingual word embedding space can be induced by projecting monolingual word embedding spaces from two languages using a self-learning paradigm without any bilingual supervisi…
Bilingual Lexicon InductionCross-Lingual Word EmbeddingsDependency ParsingDocument Classification+3A Simple Approach to Learning Unsupervised Multilingual Embeddings
Recent progress on unsupervised learning of cross-lingual embeddings in bilingual setting has given impetus to learning a shared embedding space for several languages without any supervision. A popular framework to solve…
Bilingual Lexicon InductionDependency ParsingDocument ClassificationWord Alignment+1Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models
Recently, a variety of unsupervised methods have been proposed that map pre-trained word embeddings of different languages into the same space without any parallel data. These methods aim to find a linear transformation …
Word AlignmentWord Embeddings