A Full Information Enhanced Question Answering System Based on Hierarchical Heterogeneous Crowd Intelligence Knowledge Graph

Lei Wu, Bin Guo, Hao Wang, Jiaqi Liu, Zhiwen Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the development of deep learning technology, generative question answering models based on neural networks have gradually become a mainstream research direction in academia and industry. The current question answering models fail to make full use of the multi-level knowledge embedded in the learned corpus, and the interpretability and robustness of the models in the face of attack samples have certain shortcomings. From the perspective of information theory, this paper constructs the semantic, pragmatic and syntactic knowledge contained in the large amount of crowd intelligence corpora obtained from the Internet platform into a hierarchical and heterogeneous natural language knowledge graph. The graph-based full information enhanced question answering model (GFIQA) is proposed, and the hierarchical heterogeneous knowledge graph is incorporated in the model. Through the crowd intelligence knowledge interpretation module, knowledge-enhanced generation module and single-layer anisotropic decoder, the relevant knowledge in the crowd intelligence natural language knowledge graph is appropriately selected based on the attention mechanism, and the ability of question understanding and answer generation is improved. The experimental results show that the GFIQA model has a large improvement in PPL, BLEU, and ENC (PPL: −11.76, BLEU: +0.126, ENC: + 0.232) compared with the baseline model, and can generate fluent and smooth answers with reasonable grammatical modifications and rich semantics.

Original languageEnglish
Title of host publicationComputer Supported Cooperative Work and Social Computing - 16th CCF Conference, ChineseCSCW 2021, Revised Selected Papers
EditorsYuqing Sun, Tun Lu, Buqing Cao, Hongfei Fan, Dongning Liu, Bowen Du, Liping Gao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages281-294
Number of pages14
ISBN (Print)9789811945489
DOIs
StatePublished - 2022
Event16th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2021 - Virtual, Online
Duration: 26 Nov 202128 Nov 2021

Publication series

NameCommunications in Computer and Information Science
Volume1492 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference16th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2021
CityVirtual, Online
Period26/11/2128/11/21

Keywords

  • Attention mechanism
  • Information theory
  • Knowledge graphs
  • Question answering system

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