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.

提出了一种简单而有效的分级对话理解模型HiDialog，其中应用了多个特殊Token和层级转向注意力来模拟不同问题的语义，以及异构图模块来优化学到的语义嵌入，证明该模型在对话关系提取、对话情感识别和对话行为分类等多个任务上均达到了最先进水平。

特殊标记和对话级注意力的分层对话理解