A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Understand

Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation.

• In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal.
• Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X.
• However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications).