Improving Topic Tracing with a Textual Reader for Conversational Knowledge Based Question Answering-Reference-Cited by-同舟云学术

Improving Topic Tracing with a Textual Reader for Conversational Knowledge Based Question Answering

Published:2024-06 Issue:3 Volume:8 Page:2640-2653
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Liu Zhipeng¹^ORCID,He Jing²^ORCID,Gong Tao³^ORCID,Weng Heng⁴^ORCID,Wang Fu Lee⁵^ORCID,Liu Hai⁶^ORCID,Hao Tianyong⁶^ORCID

Affiliation:

1. School of Artificial Intelligence, South China Normal University, Foshan, China

2. University of Oxford, Oxford, U.K.

3. Google Inc., New York, NY, USA

4. State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

5. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China

6. School of Computer Science, South China Normal University, Guangzhou, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Research Grants Council of the Hong Kong Special Administrative Region, China

Key Research Laboratory Construction Project of National Administration of Traditional Chinese Medicine

Project of Traditional Chinese Medicine Bureau of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10538459/10466789.pdf?arnumber=10466789

Reference49 articles.

1. A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions

2. Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph

3. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

4. Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

5. Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs