Interpretable Structure-aware Document Encoders with Hierarchical Attention
This work addresses document classification by improving performance through structure-aware encoding, though it is incremental as it builds on Tree-LSTMs.
The authors tackled the problem of creating document representations that reflect internal structure by proposing a Structure Tree-LSTM with hierarchical attention, which outperformed baselines on two datasets and achieved competitive performance on a third medical dataset.
We propose a method to create document representations that reflect their internal structure. We modify Tree-LSTMs to hierarchically merge basic elements such as words and sentences into blocks of increasing complexity. Our Structure Tree-LSTM implements a hierarchical attention mechanism over individual components and combinations thereof. We thus emphasize the usefulness of Tree-LSTMs for texts larger than a sentence. We show that structure-aware encoders can be used to improve the performance of document classification. We demonstrate that our method is resilient to changes to the basic building blocks, as it performs well with both sentence and word embeddings. The Structure Tree-LSTM outperforms all the baselines on two datasets by leveraging structural clues. We show our model's interpretability by visualizing how our model distributes attention inside a document. On a third dataset from the medical domain, our model achieves competitive performance with the state of the art. This result shows the Structure Tree-LSTM can leverage dependency relations other than text structure, such as a set of reports on the same patient.