Cage: A Hybrid Framework for Closed-Domain Conversational Agents
Abstract
Current conversational agents are primarily designed to answer user queries based on structured pre-defined utterance-response pairs. While question-answering (QA) systems extracts potential answers, to queries, from unstructured texts. However, in domain-specific settings, manual creation of query-response pairs is expensive, and domain adaptation of QA platforms is crucial. To this end, we propose Cage , a “hybrid” conversational framework seamlessly integrating structured and unstructured data to obtain precise answers for user queries – improving user experience and quality-of-service . We describe the different components combining query matching and extractive question answering , and demonstrate the multi-lingual chatbot interface provided to a user.
Cite
Text
Burgin et al. "Cage: A Hybrid Framework for Closed-Domain Conversational Agents." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26422-1_46Markdown
[Burgin et al. "Cage: A Hybrid Framework for Closed-Domain Conversational Agents." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/burgin2022ecmlpkdd-cage/) doi:10.1007/978-3-031-26422-1_46BibTeX
@inproceedings{burgin2022ecmlpkdd-cage,
title = {{Cage: A Hybrid Framework for Closed-Domain Conversational Agents}},
author = {Burgin, Edward and Dutta, Sourav and Assem, Haytham and Patel, Raj Nath},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2022},
pages = {636-640},
doi = {10.1007/978-3-031-26422-1_46},
url = {https://mlanthology.org/ecmlpkdd/2022/burgin2022ecmlpkdd-cage/}
}