Dialogue-level dependency parsing has received insufficient attention,
especially for Chinese. To this end, we draw on ideas from syntactic dependency
and rhetorical structure theory (RST), developing a high-quality
human-annotated corpus, which contains 850 dialogues and 199,803 dependencies.
Considering that such tasks suffer from high annotation costs, we investigate
zero-shot and few-shot scenarios. Based on an existing syntactic treebank, we
adopt a signal-based method to transform seen syntactic dependencies into
unseen ones between elementary discourse units (EDUs), where the signals are
detected by masked language modeling. Besides, we apply single-view and
multi-view data selection to access reliable pseudo-labeled instances.
Experimental results show the effectiveness of these baselines. Moreover, we
discuss several crucial points about our dataset and approach.

研究了汉语对话级别的依存分析，利用句法依存和修辞结构理论的思想，开发了一个人工注释的高质量语料库，探索了零样本和少样本场景，并考虑采用基于信号检测的方法将先前存在的句法依存转化为单元话语之间的未见依存的场景。通过单视图和多视图数据选择来获取可靠的伪标签实例，实验结果表明了这些基线的有效性。同时，我们还讨论了有关数据集和方法的几个关键点。