By introducing a small set of additional parameters, a probe learns to solve specific linguistic
tasks (eg, dependency parsing) in a supervised manner using feature representations (eg …

Discourse parsing is an important research area in natural language processing (NLP),
which aims to parse the discourse structure of coherent sentences. In this survey, we …

Rhetorical structure theory (RST) is a significant theory about discourse organization. With
an increasing number of research interests focus on RST, many novel parsing approaches …

In this paper, we focus on learning structure-aware document representations from data
without recourse to a discourse parser or additional annotations. Drawing inspiration from …

Clauses and sentences rarely stand on their own in an actual discourse; rather, the
relationship between them carries important information that allows the discourse to express …

Abstract The CoNLL-2015 Shared Task is on Shallow Discourse Parsing, a task focusing on
identifying individual discourse relations that are present in a natural language text. A …

Without discourse connectives, classifying implicit discourse relations is a challenging task
and a bottleneck for building a practical discourse parser. Previous research usually makes …

Discourse structures are beneficial for various NLP tasks such as dialogue understanding,
question answering, sentiment analysis, and so on. This paper presents a deep sequential …

RST-style document-level discourse parsing remains a difficult task and efficient deep
learning models on this task have rarely been presented. In this paper, we propose an …

The current state-of-the-art singledocument summarization method generates a summary by
solving a Tree Knapsack Problem (TKP), which is the problem of finding the optimal rooted …