Abstract
Interactive Task Learning (ITL) concerns learning about unforeseen domain concepts via natural interactions with human users. The learner faces a number of significant constraints: learning should be online, incremental and few-shot, as it is expected to perform tangible belief updates right after novel words denoting unforeseen concepts are introduced. In this work, we explore a challenging symbol grounding task—discriminating among object classes that look very similar—within the constraints imposed by ITL. We demonstrate empirically that more data-efficient grounding results from exploiting the truth-conditions of the teacher’s generic statements (e.g., “Xs have attribute Z.”) and their implicatures in context (e.g., as an answer to “How are Xs and Ys different?”, one infers Y lacks attribute Z).- Anthology ID:
- 2023.iwcs-1.33
- Volume:
- Proceedings of the 15th International Conference on Computational Semantics
- Month:
- June
- Year:
- 2023
- Address:
- Nancy, France
- Editors:
- Maxime Amblard, Ellen Breitholtz
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 318–331
- Language:
- URL:
- https://aclanthology.org/2023.iwcs-1.33
- DOI:
- Cite (ACL):
- Jonghyuk Park, Alex Lascarides, and Subramanian Ramamoorthy. 2023. Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse. In Proceedings of the 15th International Conference on Computational Semantics, pages 318–331, Nancy, France. Association for Computational Linguistics.
- Cite (Informal):
- Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse (Park et al., IWCS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.iwcs-1.33.pdf