Structured Dialogue Discourse Parsing
Ta-Chung Chi Alexander I. Rudnicky
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~\url{https://github.com/chijames/structured_dialogue_discourse_parsing}.}