Abstract
We analyze the use and interpretation of modal expressions in a corpus of situated human-robot dialogue and ask how to effectively represent these expressions for automatic learning. We present a two-level annotation scheme for modality that captures both content and intent, integrating a logic-based, semantic representation and a task-oriented, pragmatic representation that maps to our robot’s capabilities. Data from our annotation task reveals that the interpretation of modal expressions in human-robot dialogue is quite diverse, yet highly constrained by the physical environment and asymmetrical speaker/addressee relationship. We sketch a formal model of human-robot common ground in which modality can be grounded and dynamically interpreted.- Anthology ID:
- 2020.coling-main.373
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4222–4238
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.373
- DOI:
- 10.18653/v1/2020.coling-main.373
- Cite (ACL):
- Lucia Donatelli, Kenneth Lai, and James Pustejovsky. 2020. A Two-Level Interpretation of Modality in Human-Robot Dialogue. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4222–4238, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- A Two-Level Interpretation of Modality in Human-Robot Dialogue (Donatelli et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.coling-main.373.pdf
- Data
- MPQA Opinion Corpus