A Multi-task Approach to Learning Multilingual Representations
Karan Singla, Doğan Can, Shrikanth Shri Narayanan · 2018
We present a novel multi-task modeling approach to learning multilingual distributed representations of text.Our system learns word and sentence embeddings jointly by training a multilingual skipgram model together with a cross-lingual sentence similarity model.Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone.Our model shows competitive performance in a standard crosslingual document classification task.We also show the effectiveness of our method in a limited resource scenario.