An Unsupervised Approach for Mapping between Vector Spaces
We present a language independent, unsupervised approach for transforming word embeddings from source language to target language using a transformation matrix. Our model handles the problem of data scarcity which is faced by many languages in the world and yields improved word embeddings for words in the target language by relying on transformed embeddings of words of the source language. We initially evaluate our approach via word similarity tasks on a similar language pair - Hindi as source and Urdu as the target language, while we also evaluate our method on French and German as target languages and English as source language. Our approach improves the current state of the art results - by 13% for French and 19% for German. For Urdu, we saw an increment of 16% over our initial baseline score. We further explore the prospects of our approach by applying it on multiple models of the same language and transferring words between the two models, thus solving the problem of missing words in a model. We evaluate this on word similarity and word analogy tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Word EmbeddingsWord SimilaritySimilar Papers 제목 키워드 기반
Cross-topic distributional semantic representations via unsupervised mappings
In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation. In this work, we propose a DSM that learns multiple distributional re…
Word SimilarityBootstrapping Unsupervised Bilingual Lexicon Induction
The task of unsupervised lexicon induction is to find translation pairs across monolingual corpora. We develop a novel method that creates seed lexicons by identifying cognates in the vocabularies of related languages on…
Bilingual Lexicon InductionSemantic Textual SimilarityTranslationLearning Unsupervised Word Mapping by Maximizing Mean Discrepancy
Cross-lingual word embeddings aim to capture common linguistic regularities of different languages, which benefit various downstream tasks ranging from machine translation to transfer learning. Recently, it has been show…
Cross-Lingual Word EmbeddingsDensity EstimationMachine TranslationTransfer Learning+2Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings
Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised…
Word EmbeddingsData Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings
Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the ass…
Cross-Lingual Word EmbeddingsData AugmentationMachine TranslationTranslation+2