Dialogue discourse parsing aims to uncover the internal structure of a
multi-participant conversation by finding all the discourse~\emph{links} and
corresponding~\emph{relations}. Previous work either treats this task as a
series of independent multiple-choice problems, in which the link existence and
relations are decoded separately, or the encoding is restricted to only local
interaction, ignoring the holistic structural information. In contrast, we
propose a principled method that improves upon previous work from two
perspectives: encoding and decoding. From the encoding side, we perform
structured encoding on the adjacency matrix followed by the matrix-tree
learning algorithm, where all discourse links and relations in the dialogue are
jointly optimized based on latent tree-level distribution. From the decoding
side, we perform structured inference using the modified Chiu-Liu-Edmonds
algorithm, which explicitly generates the labeled multi-root non-projective
spanning tree that best captures the discourse structure. In addition, unlike
in previous work, we do not rely on hand-crafted features; this improves the
model's robustness. Experiments show that our method achieves new
state-of-the-art, surpassing the previous model by 2.3 on STAC and 1.5 on
Molweni (F1 scores). \footnote{Code released
at~https://github.com/chijames/structured_dialogue_discourse_parsing.}

该篇论文介绍了一种基于结构化编码和矩阵树学习算法的对话语篇分析方法，该方法通过编码和解码进行分析，不依赖手工特征，并在实验中达到了新的最优效果。