Abstract
The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by the current popular open-domain dialog systems and chatbots. Heavily relying on corpus-based machine learning techniques to encode and decode context-sensitive meanings, these systems focus on fitting a particular training dataset, but not tracking what is actually happening in a conversation, and therefore easily derail in a new context. This work sketches out a more linguistically-informed architecture to handle social talk in English, in which corpus-based methods form the backbone of the relatively context-insensitive components (e.g. part-of-speech tagging, approximation of lexical meaning and constituent chunking), while symbolic modeling is used for reasoning out the context-sensitive components, which do not have any consistent mapping to linguistic forms. All components are fitted into a Bayesian game-theoretic model to address the interactive and rational aspects of conversation.- Anthology ID:
- 2022.acl-srw.14
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Samuel Louvan, Andrea Madotto, Brielen Madureira
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 153–170
- Language:
- URL:
- https://aclanthology.org/2022.acl-srw.14
- DOI:
- 10.18653/v1/2022.acl-srw.14
- Cite (ACL):
- Alex Lưu. 2022. Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 153–170, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (Lưu, ACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.acl-srw.14.pdf