A Multi-level Neural Network for Implicit Causality Detection in Web Texts
TABLE VII: Scalability on the Constructed Corpus.
['[BOLD] Methods', '[BOLD] Metrics Accuracy', '[BOLD] Metrics Precision', '[BOLD] Metrics Recall', '[BOLD] Metrics F1-score']
Scalability on Other Dataset. We constructed a corpus, which is composed of 1340 sentences, half of these which contains causal relation. To investigate the scalability of our model, we train MCDN on the AltLex Training set and Bootstrapped set then test on the corpus. It is extracted from the SemEval-2010-Task8 dataset by filtering the sentences containing AltLex from ”Cause-Effect” relation and randomly from other relations. We conjectured that the poor performance of BERT is due to the focal loss function we used and the unbalanced data distribution between the AltLex dataset and constructed corpus. Therefore, we divided the corpus into a train set and a test set to fine-tune BERT with normal cross entropy loss function. *. It’s foreseeable from our point of view that the overall performance of MCDN is slightly worse than fine-tuned BERT because MCDN needs much fewer parameter and pre-training time. We could combine them in future works.