  Compared with standard text, understanding dialogue is more challenging for
machines as the dynamic and unexpected semantic changes in each turn. To model
such inconsistent semantics, we propose a simple but effective Hierarchical
Dialogue Understanding model, HiDialog. Specifically, we first insert multiple
special tokens into a dialogue and propose the turn-level attention to learn
turn embeddings hierarchically. Then, a heterogeneous graph module is leveraged
to polish the learned embeddings. We evaluate our model on various dialogue
understanding tasks including dialogue relation extraction, dialogue emotion
recognition, and dialogue act classification. Results show that our simple
approach achieves state-of-the-art performance on all three tasks above. All
our source code is publicly available at https://github.com/ShawX825/HiDialog.
