Abstract
The TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration (MIER-19) tackles explanation generation for answers to elementary science questions. It builds on the AI2 Reasoning Challenge 2018 (ARC-18) which was organized as an advanced question answering task on a dataset of elementary science questions. The ARC-18 questions were shown to be hard to answer with systems focusing on surface-level cues alone, instead requiring far more powerful knowledge and reasoning. To address MIER-19, we adopt a hybrid pipelined architecture comprising a featurerich learning-to-rank (LTR) machine learning model, followed by a rule-based system for reranking the LTR model predictions. Our system was ranked fourth in the official evaluation, scoring close to the second and third ranked teams, achieving 39.4% MAP.- Anthology ID:
- D19-5312
- Volume:
- Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong
- Editors:
- Dmitry Ustalov, Swapna Somasundaran, Peter Jansen, Goran Glavaš, Martin Riedl, Mihai Surdeanu, Michalis Vazirgiannis
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 90–100
- Language:
- URL:
- https://aclanthology.org/D19-5312
- DOI:
- 10.18653/v1/D19-5312
- Cite (ACL):
- Jennifer D’Souza, Isaiah Onando Mulang’, and Sören Auer. 2019. Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 90–100, Hong Kong. Association for Computational Linguistics.
- Cite (Informal):
- Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions (D’Souza et al., TextGraphs 2019)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/D19-5312.pdf