Chefbot: A Novel Framework for the Generation of Commonsense-enhanced Responses for Task-based Dialogue Systems

Strathearn, Carl; Gkatzia, Dimitra

Abstract

Conversational systems aim to generate responses that are accurate, relevant and engaging, either through utilising neural end-to-end models or through slot filling. Human-to-human conversations are enhanced by not only the latest utterance of the interlocutor, but also by recalling relevant information about concepts/objects covered in the dialogue and integrating them into their responses. Such information may contain recent referred concepts, commonsense knowledge and more. A concrete scenario of such dialogues is the cooking scenario, i.e. when an artificial agent (personal assistant, robot, chatbot) and a human converse about a recipe. We will demo a novel system for commonsense enhanced response generation in the scenario of cooking, where the conversational system is able to not only provide directions for cooking step-by-step, but also display commonsense capabilities by offering explanations of how objects can be used and provide recommendations for replacing ingredients.

Citation

Strathearn, C., & Gkatzia, D. (2021). Chefbot: A Novel Framework for the Generation of Commonsense-enhanced Responses for Task-based Dialogue Systems. In Proceedings of the 14th International Conference on Natural Language Generation (46-47)

• Conference Name: 14th International Conference on Natural Language Generation
• Conference Location: Aberdeen
• Publication Date: 2021
• Deposit Date: Dec 2, 2022
• Publisher: Association for Computational Linguistics (ACL)
• Pages: 46-47
• Book Title: Proceedings of the 14th International Conference on Natural Language Generation
• Public URL: http://researchrepository.napier.ac.uk/Output/2969151
• Publisher URL: https://aclanthology.org/2021.inlg-1.5