BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
Table 2: Performance of Ensemble system on Hinglish and Spanglish test datasets
['[EMPTY]', '[BOLD] F1  [BOLD] o', '[BOLD] F1  [BOLD] +', '[BOLD] F1  [BOLD] -', '[BOLD] F1  [BOLD] Macro', '[BOLD] Macro  [BOLD] Precision', '[BOLD] Macro  [BOLD] Recall']
Combining the results of CNN with those of the Self-Attention model was the primary motivation for using an ensemble of the two. The ensemble outperforms all our previous models, achieving a recall of 0.705 with an F1-score of 0.707 on the Hinglish test dataset and a recall of 0.696 with an F1-score of 0.725 on the Spanglish test dataset The confusion matrices for the ensemble on both datasets are shown in figure 6 and 7 (o : neutral, + : positive, - : negative). Our team was ranked 5th among 62 teams in Hinglish and 13th among 29 teams in Spanglish.