Improving Visually Grounded Sentence Representations with Self-Attention
Table 1: Classification performance on transfer tasks. We report F1-score for MRPC, Pearson coefficient for SICK-R and accuracy for most others. All sentence representations have been concatenated with ST-LN embeddings. Note that the discrepancy between results reported in this paper and the referenced paper is likely due to differences in minor implementation details and experimental environment. Our models are denoted by †.
['Method', 'MR', 'CR', 'SUBJ', 'MPQA', 'MRPC', 'SST', 'SICK-E', 'SICK-R']
Results show that incorporating self-attention mechanism in the encoder is beneficial for most tasks. However, original models were better in some tasks (CR, MPQA, MRPC), suggesting that self-attention mechanism could sometimes introduce noise in sentence features. Overall, utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks. Considering that models with self-attention employ smaller LSTM cells (1024) than those without (2048)