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In natural languages, words are used in association to construct sentences.It is not words in isolation, but the appropriate combination of hierarchicalstructures that conveys the meaning of the whole sentence. Neural networks cancapture expressive language features; however, insights into the link betweenwords and sentences are difficult to acquire automatically. In this work, wedesign a deep neural network architecture that explicitly wires lower andhigher linguistic components; we then evaluate its ability to perform the sametask at different hierarchical levels. Settling on broad text classificationtasks, we show that our model, MHAL, learns to simultaneously solve them atdifferent levels of granularity by fluidly transferring knowledge betweenhierarchies. Using a multi-head attention mechanism to tie the representationsbetween single words and full sentences, MHAL systematically outperformsequivalent models that are not incentivized towards developing compositionalrepresentations. Moreover, we demonstrate that, with the proposed architecture,the sentence information flows naturally to individual words, allowing themodel to behave like a sequence labeller (which is a lower, word-level task)even without any word supervision, in a zero-shot fashion.

Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels