Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.
the new question can be viewed as a corrupted version of a (by adding q's tokens into it), and after the subsequent pooling operations, cosine similarities between the original answer's representation and the new corrupted version are computed.