In the last decade, deep artificial neural networks have achieved astounding performance in
many natural language-processing tasks. Given the high productivity of language, these …

Despite a multitude of empirical studies, little consensus exists on whether neural networks
are able to generalise compositionally, a controversy that, in part, stems from a lack of …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Humans easily interpret expressions that describe unfamiliar situations composed from
familiar parts (" greet the pink brontosaurus by the ferris wheel"). Modern neural networks, by …

While mainstream machine learning methods are known to have limited ability to
compositionally generalize, new architectures and techniques continue to be proposed to …

Despite achieving tremendous success, existing deep learning models have exposed
limitations in compositional generalization, the capability to learn compositional rules and …

Earthquake forecasting and prediction have long and in some cases sordid histories but
recent work has rekindled interest based on advances in early warning, hazard assessment …

Humans understand novel sentences by composing meanings and roles of core language
components. In contrast, neural network models for natural language modeling fail when …

Sequence-to-sequence learning with neural networks has become the de facto standard for
sequence modeling. This approach typically models the local distribution over the next …

Despite progress across a broad range of applications, Transformers have limited success
in systematic generalization. The situation is especially frustrating in the case of algorithmic …