Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
Table 3: Results on the development and testing sets. Accuracy and F1 scores: macro-averaged and per class (S: supporting, D: denying, Q: querying, C: commenting).
['[EMPTY]', '[BOLD] Accuracy', '[BOLD] Macro F', '[BOLD] S', '[BOLD] D', '[BOLD] Q', '[BOLD] C']
The performance of our model on the testing and development set is shown in Table 3. [CONTINUE] The difference in accuracy between testing and development set is minimal, however we see significant difference in Macro-F score due to different class balance in these sets. [CONTINUE] The branch-LSTM model predicts commenting, the majority class well, however it is unable to pick out any denying, the mostchallenging under-represented class.