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Automatic question-answering is a classical problem in natural languageprocessing, which aims at designing systems that can automatically answer aquestion, in the same way as human does. In this work, we propose a deeplearning based model for automatic question-answering. First the questions andanswers are embedded using neural probabilistic modeling. Then a deepsimilarity neural network is trained to find the similarity score of a pair ofanswer and question. Then for each question, the best answer is found as theone with the highest similarity score. We first train this model on alarge-scale public question-answering database, and then fine-tune it totransfer to the customer-care chat data. We have also tested our framework on apublic question-answering database and achieved very good performance.

Automatic Question-Answering Using A Deep Similarity Neural Network