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.
翻译:对文本的量化推理是人工智能(AI)的一项艰巨任务,需要阅读理解和数字推理能力。以前的方法使用数字推理程序来代表推理过程。然而,大多数工作并不将操作者和操作者(这是数字推理程序的关键组成部分)的生成分开,从而限制了它们为复杂任务生成此类程序的能力。在本文件中,我们引入了带有适应性符号Ic编纂(ELASTIC)模型的nURIL再处理,该模型由RoBERTA作为编目者和四个模块的编译者组成:解释管理器、操作器生成器、操作器生成器和存储器。在进行复杂推理时,拉美科技中心是强有力的。此外,它通过支持不同操作者的扩展而不考虑其包含的操作数量,是无异的。实验表明,拉美科技中心在FinQA数据集上实现了68.96和65.21执行准确性和程序准确度,在数学QA数据集上实现了83.00方案精度,大大优于以往的状态模型。