DisSent: Learning Sentence Representations from Explicit Discourse Relations
Table 10: Corrected GloVe Bag-of-words sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes.
['Marker', 'All Prec', 'All Rec', 'Books 8 Prec', 'Books 8 Rec', 'Books 5 Prec', 'Books 5 Rec']
As a reference point for training task performance we present baseline performance. Note that a model which simply chose the most common class would perform with 21.79% accuracy on the ALL task, 28.35% on the Books 8 task, and 31.87% on the Books 5 task. Using either unigram, bigram and trigram bag of words or Arora et al. ’s baseline sentence representations as features to a logistic regression results in much lower performance than our DisSent classifier.