UNLPSat TextGraphs-16 Natural Language Premise Selection task: Unsupervised Natural Language Premise Selection in mathematical text using sentence-MPNet
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Date
2022-10-16
Authors
Trust, Paul
Kadusabe, Provia
Younis, Haseeb
Minghim, Rosane
Milios, Evangelos
Zahran, Ahmed
Journal Title
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Volume Title
Publisher
Association for Computational Linguistics
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Abstract
This paper describes our system for the submission to the TextGraphs 2022 shared task at COLING 2022: Natural Language Premise Selection (NLPS) from mathematical texts. The task of NLPS is about selecting mathematical statements called premises in a knowledge base written in natural language and mathematical formulae that are most likely to be used to prove a particular mathematical proof. We formulated this task as an unsupervised semantic similarity task by first obtaining contextualized embeddings of both the premises and mathematical proofs using sentence transformers. We then obtained the cosine similarity between the embeddings of premises and proofs and then selected premises with the highest cosine scores as the most probable. Our system improves over the baseline system that uses bag of words models based on term frequency inverse document frequency in terms of mean average precision (MAP) by about 23.5% (0.1516 versus 0.1228).
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Keywords
Natural Language Premise Selection
Citation
Trust, P., Kadusabe, P., Younis, H., Minghim, R., Milios, E. and Zahran, A. (2022) 'UNLPSat TextGraphs-16 Natural Language Premise Selection task: Unsupervised Natural Language Premise Selection in mathematical text using sentence-MPNet', TextGraphs-16: Graph-based Methods for Natural Language Processing, Gyeongju, Republic of Korea, 16 October, pp. 119-123. Available at: https://aclanthology.org/2022.textgraphs-1.13 (Accessed: 8 November 2022)