Learning Translations via Matrix Completion

Abstract

Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completing the matrix. This method harnesses diverse bilingual and monolingual signals, each of which may be incomplete or noisy. Our model achieves state-of-the-art performance for both high and low resource languages.

This is a late posting of an old paper as Google Scholar somehow misses indexing the ACL anthology version of the paper

References in corpus (7)

• Distributed Representations of Words and Phrases and their Compositionality
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Exploiting Similarities among Languages for Machine Translation
• Zero-Shot Learning Through Cross-Modal Transfer
• An Autoencoder Approach to Learning Bilingual Word Representations
• BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

Cited by in corpus (5)

• Sentiment-based Candidate Selection for NMT
• On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
• Characterizing Departures from Linearity in Word Translation
• Link Prediction for Temporally Consistent Networks
• "Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks