Qingxiang Wang, Chad Brown, Cezary Kaliszyk, Josef Urban
International Conference on Certified Programs and Proofs (CPP 2020), ACM pp. 85 – 98, 2020.
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doi:10.1145/3372885.3373827
Abstract
In this paper we share several experiments trying to automatically translate informal mathematics into formal mathematics. In our context informal mathematics refers to human-written mathematical sentences in the LaTeX format; and formal mathematics refers to statements in the Mizar language. We conducted our experiments against three established neural network-based machine translation models that are known to deliver competitive results on translating between natural languages. To train these models we also prepared four informal-to-formal datasets. We compare and analyze our results according to whether the model is supervised or unsupervised. In order to augment the data available for auto-formalization and improve the results, we develop a custom type-elaboration mechanism and integrate it in the supervised translation.
BibTex
@inproceedings{qwcbckju-cpp20, author = {Qingxiang Wang and Chad E. Brown and Cezary Kaliszyk and Josef Urban}, editor = {Jasmin Blanchette and Catalin Hritcu}, title = {Exploration of neural machine translation in autoformalization of mathematics in {M}izar}, booktitle = {Proceedings of the 9th {ACM} {SIGPLAN} International Conference on Certified Programs and Proofs, {CPP} 2020, New Orleans, LA, USA, January 20-21, 2020}, pages = {85--98}, publisher = {{ACM}}, year = {2020}, url = {https://doi.org/10.1145/3372885.3373827}, doi = {10.1145/3372885.3373827}, }