IJS at TextGraphs-16 Natural Language Premise Selection Task: Will Contextual Information Improve Natural Language Premise Selection?
Thi Hong Hanh Tran, Matej Martinc, Antoine Doucet, Senja Pollak
Abstract
Natural Language Premise Selection (NLPS) is a mathematical Natural Language Processing (NLP) task that retrieves a set of applicable relevant premises to support the end-user finding the proof for a particular statement. In this research, we evaluate the impact of Transformer-based contextual information and different fundamental similarity scores toward NLPS. The results demonstrate that the contextual representation is better at capturing meaningful information despite not being pretrained in the mathematical background compared to the statistical approach (e.g., the TF-IDF) with a boost of around 3.00% MAP@500.- Anthology ID:
- 2022.textgraphs-1.12
- Volume:
- Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Dmitry Ustalov, Yanjun Gao, Alexander Panchenko, Marco Valentino, Mokanarangan Thayaparan, Thien Huu Nguyen, Gerald Penn, Arti Ramesh, Abhik Jana
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 114–118
- Language:
- URL:
- https://aclanthology.org/2022.textgraphs-1.12
- DOI:
- Cite (ACL):
- Thi Hong Hanh Tran, Matej Martinc, Antoine Doucet, and Senja Pollak. 2022. IJS at TextGraphs-16 Natural Language Premise Selection Task: Will Contextual Information Improve Natural Language Premise Selection?. In Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing, pages 114–118, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- IJS at TextGraphs-16 Natural Language Premise Selection Task: Will Contextual Information Improve Natural Language Premise Selection? (Tran et al., TextGraphs 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.textgraphs-1.12.pdf