Abstract
While the problem of natural language generation from logical formulas has a long tradition, thus far little attention has been paid to ensuring that the generated explanations are optimally effective for the user. We discuss issues related to deciding what such output should look like and strategies for addressing those issues. We stress the importance of informing generation of NL explanations of logical formulas through reader studies and findings on the comprehension of logic from Pragmatics and Cognitive Science. We then illustrate the discussed issues and potential ways of addressing them using a simple demo system’s output generated from a propositional logic formula.- Anthology ID:
- 2020.nl4xai-1.9
- Volume:
- 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
- Month:
- November
- Year:
- 2020
- Address:
- Dublin, Ireland
- Venue:
- NL4XAI
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 39–43
- Language:
- URL:
- https://aclanthology.org/2020.nl4xai-1.9
- DOI:
- Cite (ACL):
- Alexandra Mayn and Kees van Deemter. 2020. Towards Generating Effective Explanations of Logical Formulas: Challenges and Strategies. In 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, pages 39–43, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Towards Generating Effective Explanations of Logical Formulas: Challenges and Strategies (Mayn & van Deemter, NL4XAI 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.nl4xai-1.9.pdf