Syntax-Aware Multi-Sense Word Embeddingsfor Deep Compositional Models of Meaning
Table 4: Different disambiguation choices for the paraphrase detection task (accuracy × 100).
['[BOLD] Model', '[BOLD] Ambig.', '[BOLD] Prior', '[BOLD] Hard DD', '[BOLD] Soft DD']
The dynamic disambiguation models and the additive baseline are compared with variations that use a simple prior disambiguation step applied on the word vectors. This is achieved by first selecting for each word the sense vector that is the closest to the average of all other word vectors in the same sentence, and then composing the selected sense vectors without further considerations regarding ambiguity. The baseline model and the prior disambiguation variants are trained as separate logistic regression classifiers.