Modelling Sentence Pairs with Tree-structured Attentive Encoder
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['[BOLD] Method', '[BOLD] Acc(%)', '[BOLD] F1(%)']
The previous approaches are: (1) Baseline, cosine similarity with tf-idf weighting; (2) RAE, recursive autoencoder with dynamic pooling; (3) combine-skip+feats, skip-thought vectors with features; (4) ABCNN-3, attention-based convolutional nets; and (5) TF-KLD, matrix factorization with supervised reweighting. First, all our models are able to outperform the baseline. We only compare our models with the neural networks-based approaches, including RAE and ABCNN-3 for a fair comparison. We find that our models do not prove to be very competitive. After a careful analysis, we conclude that the reasons are (1) our models are pure neural networks-based, we don’t add any features to identify paraphrases while the other methods have used additional features; (2) The MLP is not very suitable in this task. We attempt to replace the MLP with the cosine distance and euclidean distance in our future work. Although our models have not yet matched the SOTA performance, we obtain an improvement of +2.3 accuracy by Attentive Tree-LSTMs when we incorporate the attention into the standard Tree-LSTM. The loss function is the same as the paraphrase identification since this task is also a binary classification task. Since this dataset is a fresh and uncompleted dataset, we only compare our models with Baudis et al. \shortcitebaudis2016joint who have evaluated several models on it. It is observed that tree-structured models are more competitive than the sequential counterparts. As we expected, the attentive models can outperform all non-attentional counterparts.