Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings

Todor Mihaylov, Anette Frank

Abstract:

This paper describes our system for the CoNLL 2016 Shared Task’s supplementary task on Discourse Relation Sense
Classification. Our official submission employs a Logistic Regression classifier
with several cross-argument similarity features based on word embeddings and performs with overall F-scores of 64.13 for
the Dev set, 63.31 for the Test set and 54.69 for the Blind set, ranking first in the Overall ranking for the task. We compare the feature-based Logistic Regression classifier to different Convolutional Neural Network architectures. After the official submission we enriched our model for Non-Explicit relations by including similarities of explicit connectives with the relation arguments, and part of speech similarities based on modal verbs. This improved our Non-Explicit result by 1.46 points on the Dev set and by 0.36 points on the Blind set.