Topic-driven Distant Supervision Framework for Macro-level Discourse
  Parsing
Feng Jiang Longwang He Peifeng Li Qiaoming Zhu Haizhou Li
Discourse parsing, the task of analyzing the internal rhetorical structure of
texts, is a challenging problem in natural language processing. Despite the
recent advances in neural models, the lack of large-scale, high-quality corpora
for training remains a major obstacle. Recent studies have attempted to
overcome this limitation by using distant supervision, which utilizes results
from other NLP tasks (e.g., sentiment polarity, attention matrix, and
segmentation probability) to parse discourse trees. However, these methods do
not take into account the differences between in-domain and out-of-domain
tasks, resulting in lower performance and inability to leverage the
high-quality in-domain data for further improvement. To address these issues,
we propose a distant supervision framework that leverages the relations between
topic structure and rhetorical structure. Specifically, we propose two
distantly supervised methods, based on transfer learning and the
teacher-student model, that narrow the gap between in-domain and out-of-domain
tasks through label mapping and oracle annotation. Experimental results on the
MCDTB and RST-DT datasets show that our methods achieve the best performance in
both distant-supervised and supervised scenarios.