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Characters do not convey meaning, but sequences of characters do. We proposean unsupervised distributional method to learn the abstract meaningful units ina sequence of characters. Rather than segmenting the sequence, our DynamicCapacity Slot Attention model discovers continuous representations of theobjects in the sequence, extending an architecture for object discovery inimages. We train our model on different languages and evaluate the quality ofthe obtained representations with forward and reverse probing classifiers.These experiments show that our model succeeds in discovering units which aresimilar to those proposed previously in form, content and level of abstraction,and which show promise for capturing meaningful information at a higher levelof abstraction.

Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention