HCA: Hierarchical Compare Aggregate model for question retrieval in community question answering
作者:
Highlights:
• We propose a Hierarchical Compare Aggregate (HCA) model for question retrieval in CQA
• The HCA-model can handle the lengthy question with multiple noisy sentences
• To solve the limited training data, we propose using a sequential transfer learning
• The HCA-model does not require any external resources and task-specific features
• The HCA-model achieves the best results in the both public-domain SemEval datasets and the domain-specific AskUbuntu dataset
摘要
•We propose a Hierarchical Compare Aggregate (HCA) model for question retrieval in CQA•The HCA-model can handle the lengthy question with multiple noisy sentences•To solve the limited training data, we propose using a sequential transfer learning•The HCA-model does not require any external resources and task-specific features•The HCA-model achieves the best results in the both public-domain SemEval datasets and the domain-specific AskUbuntu dataset
论文关键词:Community question answering,Question retrieval,Hierarchical compare-aggregate model,Transfer learning,Deep learning
论文评审过程:Received 10 September 2019, Revised 26 April 2020, Accepted 31 May 2020, Available online 18 June 2020, Version of Record 18 June 2020.
论文官网地址:https://doi.org/10.1016/j.ipm.2020.102318