Modelling Sentence Pairs with Tree-structured Attentive Encoder
Table 4: Example predictions from the test set. GT: ground truth, Pred: predicted score.
['[BOLD] Dataset', '[BOLD] Sentence 1', '[BOLD] Sentence 2', '[BOLD] GT', '[BOLD] Pred']
The first group shows that our model is able to predict semantic similarity score nearly perfectly on the SICK dataset. We argue the reason is that the sentences of SICK dataset are image and video descriptions whose sentence structure is relatively simple and there are less uncommon words and named entities in the vocabulary. The second group gives us three examples on the MSRP test set. We find that our model can identify whether two fact statements are paraphrases, but fails to recognize the numbers (in group 2, line 3). We presents the examples on AI2-8grade dataset in the last group. We can observe that our model is efficient to select the false questions, while our model is difficult to select the true answers, unless the evidence of question is very strong.