Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
Table 7: Experiment results of Context Model using different pretrained word vectors.
['[BOLD] Model', '[BOLD] dev F1', '[BOLD] test F1']
To investigate the effect of pretrained word embeddings, we initialize the word embedding parameters with random initialization, GloVe pretrained word embeddings, and ELMo models. Since the official release of pretrained ELMo model has the output dimension of 1024, we do a linear transformation to reduce the output dimension to 100. The transformation matrix is part of the model parameters and are tuned during training. For GloVe, we use fixed and tuned versions of embeddings. For ELMo model, since it slows down the training significantly, we do not tune the model parameters. Fixed GloVe vectors improve the F1 score on dev and test set by 0.83% and 4.99%, respectively. The tuned version of GloVe does not improve that much, due to the fact that the dataset is small and too many parameters will cause overfitting. The pretrained ELMo model improves the F1 on the dev and test set by 10.90% and 10.24%, respectively, compared with random initialization, proving the powerfulness of ELMo model.