Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings
Todor Mihaylov, Anette Frank · 2016
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.