An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies
Table 4: Sample sentences of different similarity scores from SICK test set data. S is the predicted score and G is the ground truth.
['Sentence 1', 'Sentence 2', 'S', 'G']
These results show that the model is insusceptible to change in the voice of sentences. We also examined sentence pairs, which differed only by a single word. The changes in subject or object word affected the predicted similarity scores more than the changes in other grammatical roles like determiner or adjectives. In third example, changing man to woman in the sentence “A man is riding a horse” decreased the similarity score to 3.57, while changing a to some in the sentence “A man is preparing a dish” had little effect on scores. The above examples suggest that the effect of each grammatical relation in the overall meaning composition can be determined only by a detailed investigation of the magnitude of typed dependency embeddings.