Abstract
Textgraphs 2020 Workshop organized a shared task on ‘Explanation Regeneration’ that required reconstructing gold explanations for elementary science questions. This work describes our submission to the task which is based on multiple components: a BERT baseline ranking, an Integer Linear Program (ILP) based re-scoring and a regression model for re-ranking the explanation facts. Our system achieved a Mean Average Precision score of 0.3659.- Anthology ID:
- 2020.textgraphs-1.13
- Volume:
- Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 109–114
- Language:
- URL:
- https://aclanthology.org/2020.textgraphs-1.13
- DOI:
- 10.18653/v1/2020.textgraphs-1.13
- Cite (ACL):
- Aayushee Gupta and Gopalakrishnan Srinivasaraghavan. 2020. Explanation Regeneration via Multi-Hop ILP Inference over Knowledge Base. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 109–114, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Explanation Regeneration via Multi-Hop ILP Inference over Knowledge Base (Gupta & Srinivasaraghavan, TextGraphs 2020)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.textgraphs-1.13.pdf