Solving Math Word Problems with Teacher Supervision
By Zhenwen Liang, Xiangliang Zhang. 2021
33 citations recorded by the source corpus.
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 subtle 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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Investigating Math Word Problems using Pretrained Multilingual Language Models (2021, background): “For example, Liang and Zhang (2021) designed a teacher module to make the latent vector to match the correct solution rather than its variations.”