Answer sentence selection has been widely adopted recently for benchmarking techniques in Question Answering. Previous proposals for the task are essentially general solutions taking the form of neural networks that measure semantic similarity. In contrast, the present paper describes a simple technique to take advantage of such general-purpose tools for dealing with questions and answer sentences without changing the base system. The technique involves replacing wh-words in input questions with a word denoting the prototype of all answers. These transformed questions are passed as input to an existing neural network built for measuring semantic similarity. This technique is evaluated on two different neural network architectures over two datasets: TrecQA and WikiQA. Results of our experiments show improvement in overall accuracy across most question types we are interested in: 'who', 'when' and 'where'-type questions.
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