KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Permanent link to this recordhttp://hdl.handle.net/10754/676001
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AbstractMath word problems (MWPs) have been recently addressed with Seq2Seq models by 'translating' math problems described in natural language to a mathematical expression, following a typical encoder-decoder structure. Although effective in solving classical math problems, these models fail when a delicate variation is applied to the word expression of a math problem, and leads to a remarkably different answer. We find the failure is because MWPs with different answers but similar math formula expression are encoded closely in the latent space. We thus designed a teacher module to make the MWP encoding vector match the correct solution and disaccord from the wrong solutions, which are manipulated from the correct solution. Experimental results on two benchmark MWPs datasets verified that our proposed solution outperforms the state-of-the-art models.
CitationLiang, Z., & Zhang, X. (2021). Solving Math Word Problems with Teacher Supervision. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/485
SponsorsThis work is supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Conference/Event name30th International Joint Conference on Artificial Intelligence, IJCAI 2021