Type
Conference PaperAuthors
Liang, ZhenwenZhang, Xiangliang

KAUST Department
Computational Bioscience Research Center (CBRC)Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Date
2021-08Permanent link to this record
http://hdl.handle.net/10754/676001
Metadata
Show full item recordAbstract
Math 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.Citation
Liang, 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.2021/485Sponsors
This work is supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia.Conference/Event name
30th International Joint Conference on Artificial Intelligence, IJCAI 2021ISBN
9780999241196Additional Links
https://www.ijcai.org/proceedings/2021/485ae974a485f413a2113503eed53cd6c53
10.24963/ijcai.2021/485