Learning Bilingual Word Representations by Marginalizing Alignments

Abstract

We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data. By marginalizing over word alignments the model captures a larger semantic context than prior work relying on hard alignments. The advantage of this approach is demonstrated in a cross-lingual classification task, where we outperform the prior published state of the art.

Proceedings of ACL 2014 (Short Papers)

References in corpus (1)

• Multilingual Models for Compositional Distributed Semantics

Cited by in corpus (4)

• CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP
• Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
• A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired Binary Code Analysis
• Adversarial Structured Prediction for Multivariate Measures