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.- Anthology ID:
- 2021.inlg-1.5
- Volume:
- Proceedings of the 14th International Conference on Natural Language Generation
- Month:
- August
- Year:
- 2021
- Address:
- Aberdeen, Scotland, UK
- Editors:
- Anya Belz, Angela Fan, Ehud Reiter, Yaji Sripada
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 46–47
- Language:
- URL:
- https://aclanthology.org/2021.inlg-1.5
- DOI:
- 10.18653/v1/2021.inlg-1.5
- Cite (ACL):
- Carl Strathearn and Dimitra Gkatzia. 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, pages 46–47, Aberdeen, Scotland, UK. Association for Computational Linguistics.
- Cite (Informal):
- Chefbot: A Novel Framework for the Generation of Commonsense-enhanced Responses for Task-based Dialogue Systems (Strathearn & Gkatzia, INLG 2021)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2021.inlg-1.5.pdf