22 research outputs found
Discourse relations and conjoined VPs: automated sense recognition
Sense classification of discourse relations is a sub-task of shallow discourse parsing. Discourse relations can occur both across sentences (inter-sentential) and within sentences (intra-sentential), and more than one discourse relation can hold between the same units. Using a newly available corpus of discourse-annotated intra-sentential conjoined verb phrases, we demonstrate a sequential classification system for their multi-label sense classification. We assess the importance of each feature used in the classification, the feature scope, and what is lost in moving from gold standard manual parses to the output of an off-the-shelf parser
On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most difficult parts
in shallow discourse parsing as the relation prediction without explicit
connectives requires the language understanding at both the text span level and
the sentence level. Previous studies mainly focus on the interactions between
two arguments. We argue that a powerful contextualized representation module, a
bilateral multi-perspective matching module, and a global information fusion
module are all important to implicit discourse analysis. We propose a novel
model to combine these modules together. Extensive experiments show that our
proposed model outperforms BERT and other state-of-the-art systems on the PDTB
dataset by around 8% and CoNLL 2016 datasets around 16%. We also analyze the
effectiveness of different modules in the implicit discourse relation
classification task and demonstrate how different levels of representation
learning can affect the results.Comment: Accepted by IJCAI 202