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 customer care service for AT&T Inc. We have also tested our framework on a public question-answering database and achieved very good performance.
Paper Details
- Authors:
- Submitted On:
- 13 November 2017 - 9:10am
- Short Link:
- Type:
- Poster
- Event:
- Presenter's Name:
- Zhu Liu
- Paper Code:
- 1434
- Document Year:
- 2017
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url = {http://sigport.org/2335},
author = {Shervin Minaee; Zhu Liu },
publisher = {IEEE SigPort},
title = {Automatic Question-answering Using a Deep Similarity Neural Network},
year = {2017} }
T1 - Automatic Question-answering Using a Deep Similarity Neural Network
AU - Shervin Minaee; Zhu Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2335
ER -
