Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems
This addresses the challenge of interpreting arithmetic word problems for educational or AI applications, though it appears incremental as it builds on prior sequence labeling methods.
The paper tackles the problem of solving addition-subtraction word problems by automatically discovering hidden mathematical relations among quantities, achieving 5 and 8 points of accuracy gains on two datasets compared to prior approaches.
An arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions. This work presents a novel approach, Quantity Tagger, that automatically discovers such hidden relations by tagging each quantity with a sign corresponding to one type of mathematical operation. For each quantity, we assume there exists a latent, variable-sized quantity span surrounding the quantity token in the text, which conveys information useful for determining its sign. Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches.