Abstract
This paper discusses the challenges non-prescriptive language uses in chatbot communication create for Semantic Parsing (SP). To help SP developers improve their systems, we propose a flexible error typology based on an analysis of a sample of non-prescriptive language uses mined from a domain-specific chatbot logs. This typology is not tied to any specific language model. We also present a framework for automatically mapping these errors to the typology. Finally, we show how our framework can help evaluate SP systems from a linguistic robustness perspective. Our framework can be expanded to include new classes of errors across different domains and user demographics.- Anthology ID:
- 2024.lrec-main.158
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 1789–1794
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.158
- DOI:
- Cite (ACL):
- Anu Singh and Esme Manandise. 2024. A Typology of Errors for User Utterances in Chatbots. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1789–1794, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- A Typology of Errors for User Utterances in Chatbots (Singh & Manandise, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.158.pdf