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.

翻译：与标准文本相比，机器理解对话更具挑战性，因为每一轮对话中都存在动态且不可预测的语义变化。为建模这种不一致的语义，我们提出了一种简单但高效的层级对话理解模型HiDialog。具体而言，我们首先在对话中插入多个特殊标记，并提出轮次注意力机制以分层学习轮次嵌入。随后，利用异构图模块优化所学习的嵌入。我们在多种对话理解任务上评估了该模型，包括对话关系抽取、对话情感识别及对话行为分类。结果表明，我们提出的简单方法在上述三项任务中均取得了最优性能。所有源代码已公开于https://github.com/ShawX825/HiDialog。