Version 1
: Received: 18 September 2024 / Approved: 20 September 2024 / Online: 20 September 2024 (12:03:35 CEST)
How to cite:
Wang, C.; Liu, M.; Sui, M.; Nian, Y.; Zhou, Z. CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction. Preprints2024, 2024091617. https://doi.org/10.20944/preprints202409.1617.v1
Wang, C.; Liu, M.; Sui, M.; Nian, Y.; Zhou, Z. CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction. Preprints 2024, 2024091617. https://doi.org/10.20944/preprints202409.1617.v1
Wang, C.; Liu, M.; Sui, M.; Nian, Y.; Zhou, Z. CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction. Preprints2024, 2024091617. https://doi.org/10.20944/preprints202409.1617.v1
APA Style
Wang, C., Liu, M., Sui, M., Nian, Y., & Zhou, Z. (2024). CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction. Preprints. https://doi.org/10.20944/preprints202409.1617.v1
Chicago/Turabian Style
Wang, C., Yi Nian and Zhejie Zhou. 2024 "CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction" Preprints. https://doi.org/10.20944/preprints202409.1617.v1
Abstract
Effective communication in automated chat systems hinges on the ability to understand and respond to context. Traditional models often struggle with determining when additional context is necessary for generating appropriate responses. This paper introduces Context-Aware BERT (CA-BERT), a transformer-based model specifically fine-tuned to address this challenge. CA-BERT innovatively applies deep learning techniques to discern context necessity in multi-turn chat interactions, enhancing both the relevance and accuracy of responses. We describe the development of CA-BERT, which adapts the robust architecture of BERT with a novel training regimen focused on a specialized dataset of chat dialogues. The model is evaluated on its ability to classify context necessity, demonstrating superior performance over baseline BERT models in terms of accuracy and efficiency. Furthermore, CA-BERT's implementation showcases significant reductions in training time and resource usage, making it feasible for real-time applications. The results indicate that CA-BERT can effectively enhance the functionality of chatbots by providing a nuanced understanding of context, thereby improving user experience and interaction quality in automated systems. This study not only advances the field of NLP in chat applications but also provides a framework for future research into context-sensitive AI developments.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
