DisSent: Learning Sentence Representations from Explicit Discourse Relations
Table 5: Discourse classification task performance: Unweighted average F1 across discourse markers on the test set, and overall accuracy. Ngram-bow is a bag-of-words model built on mixture of ngram features. GloVe-bow averages word embedding with correction to frequency Arora et al. (2017). BiLSTM is the DisSent sentence encoder model. BERT is finetuned on all of the DisSent tasks.
['Model', 'All F1', 'All Acc', 'Books 8 F1', 'Books 8 Acc', 'Books 5 F1', 'Books 5 Acc']
, we achieve high levels of test performance for all discourse markers. (Though it is interesting that because, perhaps the conceptually deepest relation, is also systematically the hardest for our model.) The larger the set of discourse markers, the more difficult the task becomes, and we therefore see lower test accuracy despite larger dataset size. We conjecture that as we increase the number of discourse markers, we also increase the ambiguity between them (semantic overlap in discourse markers’ meanings), which may further explain the drop in performance. We provide per-discourse-marker performance in the Appendix.