An Autoencoder Approach to Learning Bilingual Word Representations

Understand

Cross-language learning allows us to use training data from one language to build models for a different language.

• Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora.
• In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are aligned between two languages, while not relying on word-level alignments.
• We show that by simply learning to reconstruct the bag-of-words representations of aligned sentences, within and between languages, we can in fact learn high-quality representations and do without word alignments.