REAL2: An end-to-end memory-augmented solver for math word problems

The task of math word problems has recently shown encouraging progress, e.g. in Recall and Learn (REAL), that solving problem by retrieving most similar questions based on a pre-trained memory module. In this article, we verify the effectiveness of different neural memory modules that can be trained end-to-end. Specifically, we first propose a Top-N pre-ranking process to retrieve candidate questions based on a Word2Vec model, and then we utilize a trainable memory module to re-rank the candidates to obtain the most similar Top-K questions. With this simple modification, we establish a stronger framework REAL2 that achieves state-of-the-art results. Code will be made public and we hope it will make the research of analogical learning in MWP task more accessible.