论文标题
弹性：具有自适应符号编译器的数值推理
ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler
论文作者
论文摘要
文本上的数字推理是人工智能（AI）的一项挑战，需要阅读理解和数值推理能力。先前的方法使用数值推理程序来表示推理过程。但是，大多数作品并不是分开的运算符和操作数的生成，这是数值推理程序的关键组成部分，从而限制了它们为复杂任务生成此类程序的能力。在本文中，我们用自适应符号编译器（Elastic）模型介绍了数值推理，该模型由Roberta构成，作为编码器和带有四个模块的编译器：推理管理器，操作器生成器，操作机构生成器和内存寄存器。进行复杂的推理时，弹性是强大的。同样，它是通过支持不同运营商的扩展而不关心其包含的操作数的数量来扩展的域名。实验表明，在FinQA数据集上的执行精度和程序精度的弹性达到了68.96和65.21，并且在MathQA数据集上实现了83.00的程序精度，表现优于先前的先前最新模型。
Numerical reasoning over text is a challenging task of Artificial Intelligence (AI), requiring reading comprehension and numerical reasoning abilities. Previous approaches use numerical reasoning programs to represent the reasoning process. However, most works do not separate the generation of operators and operands, which are key components of a numerical reasoning program, thus limiting their ability to generate such programs for complicated tasks. In this paper, we introduce the numEricaL reASoning with adapTive symbolIc Compiler (ELASTIC) model, which is constituted of the RoBERTa as the Encoder and a Compiler with four modules: Reasoning Manager, Operator Generator, Operands Generator, and Memory Register. ELASTIC is robust when conducting complicated reasoning. Also, it is domain agnostic by supporting the expansion of diverse operators without caring about the number of operands it contains. Experiments show that ELASTIC achieves 68.96 and 65.21 of execution accuracy and program accuracy on the FinQA dataset and 83.00 program accuracy on the MathQA dataset, outperforming previous state-of-the-art models significantly.
