Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora

Understand

We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g.

• a few hundred sentence pairs).
• Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
• Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space.