Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems

Shyam Upadhyay, Ming-Wei Chang, Kai-Wei Chang, Wen-tau Yih
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
Automatically solving algebra word problems has raised considerable interest recently. Existing state-of-the-art approaches mainly rely on learning from human annotated equations. In this paper, we demonstrate that it is possible to efficiently mine algebra problems and their numerical solutions with little to no manual effort. To leverage the mined dataset, we propose a novel structured-output learning algorithm that aims to learn from both explicit (e.g., equations) and implicit (e.g.,
more » ... ns) supervision signals jointly. Enabled by this new algorithm, our model gains 4.6% absolute improvement in accuracy on the ALG-514 benchmark compared to the one without using implicit supervision. The final model also outperforms the current state-of-the-art approach by 3%.
doi:10.18653/v1/d16-1029 dblp:conf/emnlp/UpadhyayCCY16 fatcat:l6k4uxyki5amvceffgtuvrp274