EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
Table 3: Impact of adding our binary features.
['[BOLD] Dataset', '[BOLD] w/features', '[BOLD] w/o features']
To validate the usefulness of our binary features, we performed an ablation experiment and trained our best models for each corpus without them. As seen, performance decreases in all cases, which shows that indeed these features are critical for performance, allowing the model to better capture the semantics of words missing in GloVe. In this sense, we think the usage of additional features, such as the ones derived from emotion or sentiment lexicons could indeed boost our model capabilities. This is proposed for future work.