Debiasing Counterfactual Context With Causal Inference for Multi-Turn Dialogue Reasoning-Reference-Cited by-同舟云学术

Debiasing Counterfactual Context With Causal Inference for Multi-Turn Dialogue Reasoning

Published:2024 Issue: Volume:32 Page:1125-1132
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Wang Xu¹^ORCID,Zhang Hainan²^ORCID,Zhao Shuai³^ORCID,Chen Hongshen⁴^ORCID,Ding Zhuoye⁴^ORCID,Wan Zhiguo⁵^ORCID,Cheng Bo³^ORCID,Lan Yanyan⁶^ORCID

Affiliation:

1. Hebei University of Technology, Tianjin, China

2. Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Institute of Artificial Intelligence, Beihang University, Beijing, China

3. Beijing University of Posts and Telecommunications, Beijing, China

4. Data Science Lab, JD.com, Beijing, China

5. Zhejiang Lab, Hangzhou, China

6. Tsinghua University, Beijing, China

Funder

National Key R&D Program of China

Open Foundation of State key Laboratory of Networking and Switching Technology

Beijing Advanced Innovation Fund for Future Blockchain and Privacy Computing

Frontier Cross Fund of Beihang University

Beijing Natural Science Foundation

National Natural Science Foundation of China

Guangxi Key Laboratory of Cryptography and Information Security

Zhejiang Lab

Key R&D Program of Zhejiang

Science and Technology Research Program of Higher Education Institutions in Hebei Province

Higher Education Teaching Reform Research and Practice Project of Hebei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/10304349/10361523.pdf?arnumber=10361523

Reference41 articles.

1. From Eliza to XiaoIce: challenges and opportunities with social chatbots

2. MuTual: A Dataset for Multi-Turn Dialogue Reasoning

3. Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue

4. Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering