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