Modelling Sentence Pairs with Tree-structured Attentive Encoder
Table 2: Test set results on the SICK dataset. The first group is previous results, and remaining is ours.
['[BOLD] Method', '[ITALIC] r', '[ITALIC] ρ', '[BOLD] MSE']
We first compare our results against the previous results. Kiros et al. \shortcitekiros2015skip presents an unsupervised approach to learn the universal sentence vectors without depending on a specific task. Their Combine–skip+COCO model improve the Pearson’s r to 0.8655, but a weakness is that their sentence vectors are high-dimensional vectors (2400D). Training the skip-thoughts vectors needs a lot of time and space. He et al. \shortcitehe2015multi show the effectiveness of convolutional nets with the similarity measurement layer for modelling sentence similarity. Their ConvNet outperforms ECNU with +0.027 Pearson ’s r. We can observe that dependency Tree-LSTM, combine-skip+COCO and ConvNet almost achieve the same performance and our Attentive Tree-LSTMs outperforms these three methods around +0.005 points. Comparison to ECNU, our Attentive Tree-LSTMs gains an improvement of +0.032 and achieves the state-of-the-art performance. Comparison to the non-attentional baselines (such as Tree-LSTMs), the attention mechanism (such as Attentive Tree-LSTMs) gives us a boost of around +0.007. All results highlight that our attentive Tree-RNNs are well suited for the semantic similarity task.