IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
Table 2: Ablation study results.
['[BOLD] Variation', '[BOLD] Accuracy (%)', '[BOLD] Δ%']
We performed an ablation study on a single model having obtained 69.23% accuracy on the validation set. Results are summarized in Table 2. [CONTINUE] We can observe that the architectural choice that had the greatest impact on our model was the ELMo layer, providing a 3.71% boost in performance as compared to using GloVe pre-trained word embeddings. [CONTINUE] We can further see that emoji also contributed significantly to the model's performance. [CONTINUE] Additionally, we tried using the concatenation of the max-pooled, average-pooled and last hidden states of the BiLSTM as the sentence representation, following Howard and Ruder (2018), but found out that this impacted performance negatively. [CONTINUE] Using a greater BiLSTM hidden size did not help the model, [CONTINUE] We found that using 50-dimensional part-ofspeech embeddings slightly improved results, [CONTINUE] Regarding optimization strategies, we also tried using SGD with different learning rates and a stepwise learning rate schedule as described by Conneau et al. (2018), but we found that doing this did not improve performance.