Syntax-Aware Multi-Sense Word Embeddingsfor Deep Compositional Models of Meaning
Table 6: Cross-model comparison in the paraphrase detection task.
['[EMPTY]', '[BOLD] Model', '[BOLD] Acc.', '[BOLD] F1']
We notice that using distributional properties alone cannot capture efficiently subtle aspects of a sentence, for example numbers or human names. However, even small differences on those aspects between two sentences can lead to a different classification result. Therefore, we train (using the MSPRC training data) an additional logistic regression classifier which is based not only on the embeddings similarity, but also on a few hand-engineered features. We then ensemble the new classifier (C1) with the original one. In terms of feature selection, we follow \newcitesocher2011dynamic and \newciteblacoe2012comparison and add the following features: the difference in sentence length, the unigram overlap among the two sentences, features related to numbers (including the presence or absence of numbers from a sentence and whether or not the numbers in the two sentences are the same).