We combine Recurrent Neural Networks with Tensor Product Representations to learn combinatorial representations of sequential data. This improves symbolic interpretation and systematic generalisation. Our architecture is trained end-to-end through gradient descent on a variety of simple natural language reasoning tasks, significantly outperforming the latest state-of-the-art models in single-task and all-tasks settings. We also augment a subset of the data such that training and test data exhibit large systematic differences and show that our approach generalises better than the previous state-of-the-art.
|Original language||English (US)|
|Title of host publication||Advances in Neural Information Processing Systems|
|Publisher||Neural information processing systems foundation|
|Number of pages||13|
|State||Published - Jan 1 2018|