Developing automatic Math Word Problem (MWP) solvers is a challenging task
that demands the ability of understanding and mathematical reasoning over the
natural language. Recent neural-based approaches mainly encode the problem text
using a language model and decode a mathematical expression over quantities and
operators iteratively. Note the problem text of a MWP consists of a context
part and a question part, a recent work finds these neural solvers may only
perform shallow pattern matching between the context text and the golden
expression, where question text is not well used. Meanwhile, existing decoding
processes fail to enforce the mathematical laws into the design, where the
representations for mathematical equivalent expressions are different. To
address these two issues, we propose a new encoder-decoder architecture that
fully leverages the question text and preserves step-wise commutative law.
Besides generating quantity embeddings, our encoder further encodes the
question text and uses it to guide the decoding process. At each step, our
decoder uses Deep Sets to compute expression representations so that these
embeddings are invariant under any permutation of quantities. Experiments on
four established benchmarks demonstrate that our framework outperforms
state-of-the-art neural MWP solvers, showing the effectiveness of our
techniques. We also conduct a detailed analysis of the results to show the
limitations of our approach and further discuss the potential future work. Code
is available at this https URL.

该研究提出了一种基于编码器 - 解码器的模型，完全利用问题文本并保留逐步的交换律，以产生无论数量的排列方式如何都具有不变性的表达式嵌入，并进一步对问题文本进行编码以指导解码过程，可以提高数学问题自动求解程序的性能。