Towards UniversalParaphrastic Sentence Embeddings
Table 5: Results from supervised training of each compositional architecture on similarity, entailment, and sentiment tasks. The last column shows results regularizing to our universal parameters from the models in Table 2. The first row shows Pearson’s r×100 and the last two show accuracy.
['Task', 'word averaging', 'proj.', 'DAN', 'RNN', 'LSTM (no o.g.)', 'LSTM (o.g.)', 'w/  [ITALIC] universal regularization']
We again find strong performance for the word averaging models for both similarity and entailment, outperforming the LSTM. However, for sentiment classification, we see a different trend. The LSTM now performs best, achieving 89.2% on the coarse-grained sentiment classification task. This result, to our knowledge, is the new state of the art on this task. We find that using word averaging as the compositional architecture outperforms the other architectures for similarity and entailment. However, for sentiment classification, the LSTM is much stronger than the averaging models. This suggests that the superiority of a compositional architecture can vary widely depending on the evaluation, and motivates future work to compare these architectures on additional tasks. Interestingly, it seems that regularizing to our universal parameters significantly improves results for the similarity and entailment tasks which are competitive or better than the state-of-the-art, but harms the LSTM’s performance on the sentiment classification task.