Abstract
For situated agents to effectively engage in natural-language interactions with humans, they must be able to refer to entities such as people, locations, and objects. While classic referring expression generation (REG) algorithms like the Incremental Algorithm (IA) assume perfect, complete, and accessible knowledge of all referents, this is not always possible. In this work, we show how a previously presented consultant framework (which facilitates reference resolution when knowledge is uncertain, heterogeneous and distributed) can be used to extend the IA to produce DIST-PIA, a domain-independent algorithm for REG under uncertain, heterogeneous, and distributed knowledge. We also present a novel framework that can be used to evaluate such REG algorithms without conflating the performance of the algorithm with the performance of classifiers it employs.- Anthology ID:
- W17-3511
- Volume:
- Proceedings of the 10th International Conference on Natural Language Generation
- Month:
- September
- Year:
- 2017
- Address:
- Santiago de Compostela, Spain
- Editors:
- Jose M. Alonso, Alberto Bugarín, Ehud Reiter
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 75–84
- Language:
- URL:
- https://aclanthology.org/W17-3511
- DOI:
- 10.18653/v1/W17-3511
- Cite (ACL):
- Tom Williams and Matthias Scheutz. 2017. Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework. In Proceedings of the 10th International Conference on Natural Language Generation, pages 75–84, Santiago de Compostela, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework (Williams & Scheutz, INLG 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-3511.pdf