Abstract
In semantic parsing of geographical queries against real-world databases such as OpenStreetMap (OSM), unique correct answers do not necessarily exist. Instead, the truth might be lying in the eye of the user, who needs to enter an interactive setup where ambiguities can be resolved and parsing mistakes can be corrected. Our work presents an approach to interactive semantic parsing where an explicit error detection is performed, and a clarification question is generated that pinpoints the suspected source of ambiguity or error and communicates it to the human user. Our experimental results show that a combination of entropy-based uncertainty detection and beam search, together with multi-source training on clarification question, initial parse, and user answer, results in improvements of 1.2% F1 score on a parser that already performs at 90.26% on the NLMaps dataset for OSM semantic parsing.- Anthology ID:
- 2021.splurobonlp-1.6
- Volume:
- Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Malihe Alikhani, Valts Blukis, Parisa Kordjamshidi, Aishwarya Padmakumar, Hao Tan
- Venue:
- splurobonlp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 53–59
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2021.splurobonlp-1.6/
- DOI:
- 10.18653/v1/2021.splurobonlp-1.6
- Cite (ACL):
- Michael Staniek and Stefan Riezler. 2021. Error-Aware Interactive Semantic Parsing of OpenStreetMap. In Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics, pages 53–59, Online. Association for Computational Linguistics.
- Cite (Informal):
- Error-Aware Interactive Semantic Parsing of OpenStreetMap (Staniek & Riezler, splurobonlp 2021)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2021.splurobonlp-1.6.pdf