Study of Recurrent Neural Network models used for evaluating Numerical Expressions

Numerical expression evaluation from sentences using Natural Language processing is a well-known problem domain. Evaluation of noise-free mathematical expressions has been done previously to assess the ability of the recurrent neural network to solve numerical expressions. However, the evaluation of numerical expressions when present in noisy format, i.e., operator and operands are represented “in-word” and mixed numerical format, are not well nourished till now. This paper presents a comparative study to analyze the ability of sequence-to-sequence neural network based Encoder-Decoder models to calculate the numerical output of the noisy expressions. We have treated the problem as a sequence-to-sequence based classification problem instead of a regression problem as considered earlier, to deal with the noisy data. Performance of three different Encoder-Decoder models namely Unidirectional, Bidirectional, and Attention based Encoder-Decoder LSTM models are used to evaluate the noisy expressions. It has been experimentally observed that Bidirectional and Attention based Encoder-Decoder LSTM models perform better than Unidirectional Encoder-Decoder model and the sequence-to-sequence model.