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