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Deep neural networks have become the dominant approach in natural languageprocessing (NLP). However, in recent years, it has become apparent that thereare shortcomings in systematicity that limit the performance and dataefficiency of deep learning in NLP. These shortcomings can be clearly shown inlower-level artificial tasks, mostly on synthetic data. Abstract patterns arethe best known examples of a hard problem for neural networks in terms ofgeneralisation to unseen data. They are defined by relations between items,such as equality, rather than their values. It has been argued that theselow-level problems demonstrate the inability of neural networks to learnsystematically. In this study, we propose Embedded Relation Based Patterns(ERBP) as a novel way to create a relational inductive bias that encourageslearning equality and distance-based relations for abstract patterns. ERBP isbased on Relation Based Patterns (RBP), but modelled as a Bayesian prior onnetwork weights and implemented as a regularisation term in otherwise standardnetwork learning. ERBP is is easy to integrate into standard neural networksand does not affect their learning capacity. In our experiments, ERBP priorslead to almost perfect generalisation when learning abstract patterns fromsynthetic noise-free sequences. ERBP also improves natural language models onthe word and character level and pitch prediction in melodies with RNN, GRU andLSTM networks. We also find improvements in in the more complex tasks oflearning of graph edit distance and compositional sentence entailment. ERBPconsistently improves over RBP and over standard networks, showing that itenables abstract pattern learning which contributes to performance in naturallanguage tasks.

Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling