Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning

By Zhou Zhao, Hanqing Lu, Vincent Wenchen Zheng, Deng Cai, Xiaofei He, Yueting Zhuang. 2017

58 citations recorded by the source corpus.

Nowadays the community-based question answering (CQA) sites become the popular Internet-based web service, which have accumulated millions of questions and their posted answers over time. Thus, question answering becomes an essential problem in CQA sites, which ranks the high-quality answers to the given question. Currently, most of the existing works study the problem of question answering based on the deep semantic matching model to rank the answers based on their semantic relevance, while ignoring the authority of answerers to the given question. In this paper, we consider the problem of community-based question answering from the viewpoint of asymmetric multi-faceted ranking network embedding. We propose a novel asymmetric multi-faceted ranking network learning framework for community-based question answering by jointly exploiting the deep semantic relevance between question-answer pairs and the answerers' authority to the given question. We then develop an asymmetric ranking network learning method with deep recurrent neural networks by integrating both answers' relative quality rank to the given question and the answerers' following relations in CQA sites. The extensive experiments on a large-scale dataset from a real world CQA site show that our method achieves better performance than other state-of-the-art solutions to the problem.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Xiao
2. Yueting Zhuang
3. Zhou Zhao
4. Long Chen
5. Chenyou Fan
6. Tat-seng Chua
7. Devshree Patel
8. Ratnam Parikh

In Computer Vision and Pattern Recognition

1. Jun Xiao
2. Yueting Zhuang
3. Zhou Zhao
4. Long Chen
5. Yunan Ye
6. Chenyou Fan
7. Cordelia Schmid
8. Dacheng Tao

In Information Systems

1. Jun Xiao
2. Zhou Zhao

Citations in the corpus

• Video Question Answering via Attribute-Augmented Attention Network Learning (2017, methodology): “Although existing methods have achieved promising performance in image questionanswering task, they may still be ineffective applied to the problem of video question answering due to the lack of modeling the temporal dynamics of video contents [21, 22].”