Stance Prediction for Contemporary Issues: Data and Experiments
Table 3: Evaluation results; P.:Precision, R.:Recall, ∗: p-value ≤0.001; †: p-value ≤0.0001
['Model', 'P.', 'R.', 'F1']
It was expected that fine-tuning BERT with a pair of input achieves a competitive performance among other baselines; but it shows that even with a shallow concatenation of the question and perspective (unary input), BERT can achieve consistent results. Moreover, models that take BERT representation in feature selection mode (without fine-tuning), e.g. XML-CNN(BERT), show better stance detection performance than other token embeddings.