EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
Table 2: Summary of the best results.
['[BOLD] Corpus', '[BOLD] Dropout', '[BOLD] Embeddings', '[ITALIC] λ', '[ITALIC] h', '[BOLD] EmoAtt  [ITALIC] ρdev', '[BOLD] EmoAtt  [ITALIC] ρtest', '[BOLD] Baseline  [ITALIC] ρdev', '[BOLD] Baseline  [ITALIC] ρtest']
In this section we report the results of the experiments we performed to test our proposed model. Moreover, the model manages to do so without any additional resources, except pre-trained word embeddings. These results are, however, reversed for the test dataset, where our model performs worse than the baseline. This shows that the model is not able to generalize well, which we think is related to the missing semantic information due to the vocabulary gap we observed between the datasets and the GloVe embeddings.