State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

Word embeddings have been found useful for many NLP tasks, including part-of-speech
tagging, named entity recognition, and parsing. Adding multilingual context when learning …

Unsupervised word translation from non-parallel inter-lingual corpora has attracted much
research interest. Very recently, neural network methods trained with adversarial loss …

Statistical machine translation (SMT) performance suffers when models are trained on only
small amounts of parallel data. The learned models typically have both low accuracy …

Bilingual lexicon induction is the task of inducing word translations from monolingual
corpora in two languages. In this article we present the most comprehensive analysis of …

We introduce a novel discriminative latent-variable model for the task of bilingual lexicon
induction. Our model combines the bipartite matching dictionary prior of Haghighi et …

We study the problem of bilingual lexicon induction (BLI) in a setting where some translation
resources are available, but unknown translations are sought for certain, possibly domain …

Abstract The multilingual Paraphrase Database (PPDB) is a freely available automatically
created resource of paraphrases in multiple languages. In statistical machine translation …

We use bilingual lexicon induction techniques, which learn translations from monolingual
texts in two languages, to build an end-to-end statistical machine translation (SMT) system …