DialCL-Bias: A Semantic and Contextual Framework to Identify Social Bias in Dialogue
- Resource Type
- Conference
- Authors
- Cai, Ziyan; Wu, Dingjun; Li, Ping
- Source
- 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on. :2439-2444 Oct, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Technological innovation
Annotations
Semantics
Social sciences
Self-supervised learning
Chatbots
- Language
- ISSN
- 2577-1655
The content generated by dialogue systems has been found to contain social bias due to the social bias in the corpus and the algorithm design. We propose a Dialogue-based Contrastive Learning for identifying social Bias (DialCL-Bias) framework in dialogue based on two dimensions-relevance and fine-grain. For the identification of social bias relevance, we propose a Dialogue-based Semantic Contrastive Learning (DialSCL) method. DialSCL converts simple dichotomy into triple input and decomposes the classification target based on data annotation rules for achieving semantic-level classification. We use a Key Words and Cosine Similarity (KWCS) method to construct difficult samples and achieve improvement in DialCCL. For the identification of fine-grained social bias, we propose a Dialogue-based Context Contrastive Learning (DialCCL) method. DialCCL simultaneously learns the context-level features of samples and the parameters of classifiers in the same space to naturally generate a classifier. Finally, we measure the degree of bias in the open-domain dialogue corpus and the responses of ChatGPT by using the social bias identification classifiers.