Enhancing Sentence Embedding with Generalized Pooling
Table 3: Accuracies of the models on the Yelp and Age dataset.
['[BOLD] Model', '[BOLD] Yelp', '[BOLD] Age']
The BiLSTM with self-attention proposed by \newciteDBLP:journals/corr/LinFSYXZB17 achieves better result than CNN and BiLSTM with max pooling. One of our baseline models using max pooling on BiLSTM achieves accuracies of 65.00% and 82.30% on the Yelp and the Age dataset respectively, which is already better than the self-attention model proposed by \newciteDBLP:journals/corr/LinFSYXZB17. We also show that the results of baseline with mean pooling and last pooling, in which mean pooling achieves the best result on the Yelp dataset among three baseline models and max pooling achieves the best on the Age dataset among three baselines. Our proposed generalized pooling method obtains further improvement on these already strong baselines, achieving 66.55% on the Yelp dataset and 82.63% on the Age dataset (statistically significant p<0.00001 against best baselines), which are also new state of the art performances on these two datasets.